Image recognition system and method based on deep learning

Abstract

The invention provides an image recognition system and method based on deep learning, and the system comprises a self-adaptive optical collection module, a heterogeneous preprocessing pipeline, a hierarchical reconfigurable convolutional network, a multi-dimensional training optimization engine and a cross-modal verification output interface, aperture parameters are dynamically adjusted through deep reinforcement learning; the heterogeneous preprocessing pipeline comprises a quantum noise modeling non-local mean noise reduction unit, a double-discriminator generative adversarial network enhancement unit and a dynamic normalization unit; the hierarchical reconfigurable convolutional network adopts a staged feature distillation structure and comprises a separable convolution module, a mixed pooling layer and a three-dimensional attention fusion module. According to the invention, through a multi-modal data fusion and dynamic optimization mechanism, the image acquisition quality in a complex illumination and noise scene is improved, the adaptability of the model to different environments is enhanced, and all modules work cooperatively to realize an end-to-end efficient identification process.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition, and in particular, to an image recognition system and method based on deep learning. Background Art

[0002] The traditional image recognition system still has the following limitations:

[0003] Strong dependence on data quality: In complex environments such as noise and uneven illumination, traditional acquisition modules (such as RGB cameras) are prone to losing key features.

[0004] Insufficient model generalization: The static network structure is difficult to adapt to changing physical scenarios (such as material reflection and occlusion), and adversarial sample attacks are prone to causing misjudgments.

[0005] Limited computing resources: High-precision models have a large number of parameters and are difficult to run in real time on edge devices, and traditional quantization methods have significant accuracy losses.

[0006] Multi-task conflict: The optimization objectives of tasks such as classification and detection are inconsistent, resulting in difficulty in evenly improving the model performance.

[0007] Therefore, an image recognition system and method based on deep learning are proposed. Summary of the Invention

[0008] The present invention aims to solve the problems raised in the background art, and provides an image recognition system and method based on deep learning.

[0009] The specific technical solutions are as follows:

[0010] An image recognition system based on deep learning, comprising:

[0011] An adaptive optical acquisition module, integrating a polarization filter array and a multispectral sensor, and dynamically adjusting the aperture parameters through deep reinforcement learning;

[0012] A heterogeneous preprocessing pipeline, including non-local mean denoising with quantum noise modeling, enhancement by a dual discriminator generative adversarial network, and a dynamic normalization unit;

[0013] A hierarchical reconfigurable convolutional network, adopting a staged feature distillation structure, including separable convolution, a hybrid pooling layer, and a three-dimensional attention fusion module;

[0014] A multi-dimensional training optimization engine, integrating meta-learning initialization, a dynamic task weighted loss function, and progressive model quantization;

[0015] A cross-modal verification output interface, equipped with confidence calibration and visual decision traceability functions.

[0016] The above-mentioned deep learning-based image recognition system, wherein the dual discriminator generative adversarial network includes:

[0017] a) The local texture discriminator uses 5-layer dilated convolution (dilation rate = 2) to extract fine-grained features;

[0018] b) The global semantic discriminator integrates a pyramid pooling module, and the multi-scale pooling kernel sizes are {1, 3, 6, 8};

[0019] c) Channel-space dual attention gating is embedded at the skip connections of the generator, and the calculation formula for the gating weight is:

[0020]

[0021] Where:

[0022] σ: Sigmoid activation function, compressing the output to the interval [0, 1];

[0023] Conv |x| : Convolution operation, |x| represents the convolution kernel size (such as 3×3);

[0024] Att channel and Att spatial are the channel and spatial attention weights respectively;

[0025] Feature concatenation operation.

[0026] The above-mentioned deep learning-based image recognition system, wherein the hierarchical reconfigurable convolutional network uses a parametric Swish activation function:

[0027]

[0028] Where β is a learnable scaling factor that controls the non-linearity intensity of the activation function (initial value 0.1 - 1.0), γ is a curvature control parameter that adjusts the smoothness of the function (initial value 0.01 - 0.1), and the two are jointly optimized through backpropagation; x: input feature tensor.

[0029] The above-mentioned deep learning-based image recognition system, wherein the calculation formula for the dynamic task weighted loss function is:

[0030]

[0031] Where λ is the temperature coefficient, which linearly increases with the number of training epochs t: λ t = 0.1×(1 + t / T);

[0032] (initial value 0.1, T is the total number of training epochs);

[0033] θt: The angle between the classification task gradient and the detection task gradient (unit: radian);

[0034] Lcls: Classification loss function (such as cross entropy);

[0035] Ldet: Detection loss function (such as SmoothL1).

[0036] The above-mentioned deep learning-based image recognition system, wherein the adaptive optical acquisition module includes:

[0037] a) A polarization-sensitive CMOS array that supports synchronous capture of four-way polarized light;

[0038] b) A dynamic aperture control system, the state space of which is defined as a triple of ambient illuminance, target motion vector, and scene depth distribution;

[0039] c) The multi-spectral fusion unit adopts a spectral domain attention mechanism to calculate the weights of each band:

[0040] w λ = σ(MLP(FFT(I λ ));

[0041] And generate a fused image:

[0042]

[0043] FFT: Fast Fourier Transform, which converts an image to the frequency domain;

[0044] MLP: Multi-Layer Perceptron, which generates band weights;

[0045] λ0: Central wavelength of the target band (such as 550nm in the visible light band);

[0046] σ: Gaussian kernel bandwidth, which controls the fusion range (value range 10 - 50nm);

[0047] σ(·): Sigmoid function, which normalizes the weights to [0, 1].

[0048] The above-mentioned deep learning-based image recognition system, wherein the calculation formula of the three-dimensional attention fusion module is:

[0049]

[0050] Where GAPd and GMPd respectively represent pooling operations in the channel (C), spatial (S), and depth (D) dimensions, and Wd is a learnable dimension weight matrix;

[0051] GAPd: Global average pooling in the channel (C), spatial (S), and depth (D) dimensions;

[0052] GMPd: Global Max Pooling for the corresponding dimension;

[0053] Wd: Learnable dimension weight matrix (dimension 3×1×1×1);

[0054] dim(F): Number of channels of the feature map;

[0055] ☉: Element-wise multiplication.

[0056] The above-mentioned deep learning-based image recognition system, wherein the multi-dimensional training optimization engine includes a data augmentation module based on Neural Radiance Field (NeRF), and its rendering equation is:

[0057]

[0058] where Φ is a three-dimensional radiance field function that models the geometric and material properties of the scene, β is a differentiable rendering parameter (such as lighting, reflectivity) to generate adversarial samples with real physical properties; θ, φ are the pitch angle and azimuth angle of the viewing angle (unit: radian); R is a differentiable renderer to generate images with physical properties.

[0059] The above-mentioned deep learning-based image recognition system, wherein the progressive model quantization algorithm includes:

[0060] a) Channel importance scoring:

[0061] sc is the channel importance score, and the larger the value, the more important the channel; wc (i) is the weight parameter of the i-th channel;

[0062] b) Adaptive threshold pruning: Prune the channel when s c <τ t ·max(s), and the threshold τ t = τ0·(1 - t / T) decays with training, τ0 is the initial pruning threshold (0.01 - 0.05); t is the current training round, and T is the total number of rounds.

[0063] c) Quantization compensation fine-tuning: Perform weight distribution calibration based on KL divergence after each 8-bit quantization.

[0064] The above-mentioned deep learning-based image recognition system, wherein it further includes a real-time performance adjustment module, and its inference accuracy dynamic control equation is:

[0065] k is the environmental complexity factor, and the larger the value, the more aggressive the adjustment (0.5 - 2.0); t0 is the reference time offset (system initialization parameter); Pmax is the maximum inference accuracy of the model (such as 99%);

[0066] When the frame rate FPS < FPSmin, automatically reduce the computation amount to Ptarget, where k is the environmental complexity factor.

[0067] The present invention also provides an image recognition method for an image recognition system based on deep learning, including the following steps:

[0068] S1. Multimodal image acquisition:

[0069] Synchronously capture four-way polarized light images through a polarization-sensitive CMOS array, and use a multispectral sensor to obtain spectral domain data of N bands. Dynamically adjust the aperture value F and the exposure time according to the ambient light intensity parameter to generate a group of original input images Where:

[0070] θ ∈ {0°, 45°, 90°, 135°} represents the four-way polarized light acquisition direction;

[0071] λ ∈ Λ represents the multispectral band set, Λ = {λ1, λ2,..., λ N};

[0072] is the polarized light image corresponding to the polarization direction θ;

[0073] I λ The corresponding spectral image;

[0074] S2. Heterogeneous joint preprocessing:

[0075] a) Quantum noise reduction processing: Apply noise modeling based on the Schrödinger equation to the original image:

[0076]

[0077] Where the kernel function K satisfies Simulate the quantum tunneling effect for noise suppression, where:

[0078] σ q : Quantum noise standard deviation, used to control the noise suppression intensity;

[0079] m: Simulated noise particle mass (physical parameter, unit: kg);

[0080] Reduced Planck constant;

[0081] E: Total energy of noise particles (unit: J);

[0082] V(x, y): Two-dimensional space potential energy function, describing the local noise barrier distribution of the image;

[0083] b) Dual discriminator enhancement: Input the denoised image into the generator G to generate an enhanced image

[0084] I enh = G(I denoised ), where the loss function of G is:

[0085] Where:

[0086] D1, D2: are the local texture discriminator and the global semantic discriminator respectively;

[0087] E: mathematical expectation operation;

[0088] λ per : perceptual loss weight coefficient (hyperparameter, value range [0,1]);

[0089] φ(·): the feature extractor of VGG19, used to extract high-level semantic features of images;

[0090] The square of the L2 norm, measuring the feature difference between the generated image and the original image;

[0091] S3. Hierarchical Feature Extraction and Fusion

[0092] Extract multi-scale features through a hierarchical reconstructable convolutional network, and adopt a parametric Swish activation function:

[0093] Apply a three-dimensional attention fusion module:

[0094]

[0095] (C, S, D): represent the channel, spatial, and depth dimensions respectively;

[0096] Where GAPd and GMPd: average pooling and max pooling operations for the corresponding dimensions;

[0097] Wd: learnable dimension weight matrix; ⊙: element-wise multiplication; σ: Sigmoid activation function;

[0098] S4. Dynamic Task Weighting:

[0099] Allocate weights according to the consistency of the gradient directions of the classification and detection tasks:

[0100]

[0101] NeRF Data Augmentation:

[0102] Generate physically realistic adversarial samples using neural radiance fields:

[0103]

[0104] S5. Progressive Model Quantization and Inference:

[0105] a) Channel Pruning: Prune inefficient channels according to importance scores: When s c < τ t · max(s), pruning is performed. The threshold τ t = τ0·(1 - t / T);

[0106] b) Quantization Calibration: After performing 8-bit quantization, adjust the weight distribution based on the KL divergence;

[0107] S6. Real-time Performance Dynamic Adjustment:

[0108] Dynamically adjust the inference accuracy according to the environmental complexity:

[0109]

[0110] When the frame rate FPS < FPSmin, reduce the computational amount to Ptarget;

[0111] S7. Cross-modal Verification and Output:

[0112] Adjust the prediction probability through the confidence calibration module:

[0113] Pcalibrated = σ(log(p) - μ·U(x));

[0114] where U(x) is the uncertainty measure; μ is the uncertainty calibration coefficient;

[0115] Visualize the decision traceability and output the final recognition result.

[0116] The present invention has the following beneficial effects:

[0117] 1. Environmental Adaptability: Through the fusion of polarized light and multispectral data and dynamic aperture control, the system can still capture high-quality images in extreme lighting (strong reflection, low illumination) scenarios.

[0118] 2. Robustness Enhancement: Quantum noise reduction and NeRF data enhancement technologies effectively suppress noise interference and enhance the generalization ability of the model to physical property changes.

[0119] 3. Efficient Inference: Progressive quantization and resource-aware optimization enable the model to significantly reduce the computational complexity while maintaining high accuracy, and are suitable for mobile and embedded devices.

[0120] 4. Multi-task Collaboration: Dynamic task weighting and three-dimensional attention mechanism realize the collaborative optimization of tasks such as classification and detection, and improve the comprehensive recognition accuracy in complex scenarios.

[0121] 5. Trusted Decision Output: Confidence Calibration and Visual Traceability Function, enhancing the interpretability of results and supporting the application requirements of key decision-making scenarios (such as medical diagnosis and autonomous driving). Brief Description of the Drawings

[0122] Figure 1 It is a schematic diagram of the architecture of the image recognition system based on deep learning provided by an embodiment of the present invention;

[0123] Figure 2 It is a flowchart of the image recognition method based on deep learning provided by an embodiment of the present invention;

[0124] Figure 3 It is a bar chart comparing the PSNR and SSIM of the dual-discriminator GAN and the single-discriminator GAN provided by an embodiment of the present invention;

[0125] Figure 4 It is a line chart comparing the training curves of ReLU and parametric Swish provided by an embodiment of the present invention;

[0126] Figure 5 It is a heat map of the gradient direction consistency of fixed weights and dynamic weighting provided by an embodiment of the present invention;

[0127] Figure 6 It is a bar chart comparing the classification accuracies of RGB fusion and spectral domain attention fusion provided by an embodiment of the present invention;

[0128] Figure 7 It is a bar chart comparing the target recognition accuracies of two-dimensional attention and three-dimensional attention provided by an embodiment of the present invention;

[0129] Figure 8 It is a bar chart comparing the adversarial accuracies of the model before and after enhancement under FGSM and PGD attacks provided by an embodiment of the present invention;

[0130] Figure 9 It is a bar chart comparing the inference speed and accuracy loss before and after quantization provided by an embodiment of the present invention;

[0131] Figure 10 It is a bar chart comparing the frame rate fluctuations and resource occupations under different lighting conditions provided by an embodiment of the present invention;

[0132] Figure 11 It is a bar chart comparing the COCO mAP and ImageNet-C accuracies of the traditional system and the overall system provided by an embodiment of the present invention. Detailed Embodiments

[0133] The technical solution of the present invention will be further described below with reference to the drawings and through specific embodiments.

[0134] Among them, the attached drawings are only for illustrative purposes, showing only schematic diagrams rather than physical diagrams, and should not be construed as a limitation to this patent; in order to better illustrate the embodiments of the present invention, some components in the attached drawings will be omitted, enlarged or reduced, which does not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the attached drawings may be omitted.

[0135] In the attached drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components; in the description of the present invention, it should be understood that if terms such as "upper", "lower", "left", "right", "inner", "outer", etc. are used to indicate the orientation or positional relationship, it is based on the orientation or positional relationship shown in the attached drawings. This is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms used to describe the positional relationship in the attached drawings are only for illustrative purposes and should not be construed as a limitation to this patent. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0136] In the description of the present invention, unless otherwise clearly specified and limited, if terms such as "connection" are used to indicate the connection relationship between components, this term should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0137] The image recognition system based on deep learning provided in this embodiment, as Figure 1 shown, includes an adaptive optical acquisition module, a heterogeneous preprocessing pipeline, a hierarchical reconfigurable convolutional network, a multi-dimensional training optimization engine, and a cross-modal verification output interface, where:

[0138] The adaptive optical acquisition module integrates a polarization filtering array and a multi-spectral sensor, and dynamically adjusts the aperture parameters through deep reinforcement learning;

[0139] The heterogeneous preprocessing pipeline includes non-local mean denoising with quantum noise modeling, dual discriminator generative adversarial network enhancement, and dynamic normalization units;

[0140] The hierarchical reconfigurable convolutional network adopts a stage-based feature distillation structure, including separable convolution, hybrid pooling layers, and three-dimensional attention fusion modules;

[0141] The multi-dimensional training optimization engine integrates meta-learning initialization, a dynamic task weighted loss function, and progressive model quantization;

[0142] The cross-modal verification output interface is equipped with confidence calibration and visualization decision traceability functions.

[0143] The image recognition system based on deep learning adopting the above technical solution improves the image acquisition quality in complex lighting and noise scenarios through multi-modal data fusion and dynamic optimization mechanisms, enhances the adaptability of the model to different environments, and each module works together to achieve an end-to-end efficient recognition process.

[0144] Among them, the dual discriminator generative adversarial network includes:

[0145] a) The local texture discriminator uses 5-layer dilated convolution (dilation rate = 2) to extract fine-grained features;

[0146] b) The global semantic discriminator integrates a pyramid pooling module, and the multi-scale pooling kernel sizes are {1, 3, 6, 8};

[0147] c) A channel-spatial dual attention gate is embedded at the skip connection of the generator, and its gate weight calculation formula is:

[0148]

[0149] Where:

[0150] σ: Sigmoid activation function, compressing the output to the interval [0, 1];

[0151] Conv |x| : Convolution operation, |x| represents the convolution kernel size (such as 3×3);

[0152] Att channel and Att spatial are the channel and spatial attention weights respectively;

[0153] Feature concatenation operation.

[0154] This dual discriminator generative adversarial network significantly improves the ability to retain details in image enhancement and avoids the artifact problem generated by traditional GANs through the collaborative optimization of the local texture discriminator and the global semantic discriminator.

[0155] Among them, the hierarchical reconfigurable convolutional network adopts a parametric Swish activation function:

[0156]

[0157] Where β is a learnable scaling factor that controls the non-linearity intensity of the activation function (initial value 0.1 - 1.0), γ is a curvature control parameter that adjusts the smoothness of the function (initial value 0.01 - 0.1), and the two are jointly optimized through backpropagation; x: input feature tensor.

[0158] The hierarchical reconfigurable convolutional network adopts a parametric Swish activation function, which can enhance the network's non-linear expression ability, balance the stability of gradient propagation and the sensitivity of feature extraction, and improve the convergence efficiency of deep networks.

[0159] Among them, the calculation formula of the dynamic task weighted loss function is:

[0160]

[0161] where λ is the temperature coefficient, which linearly increases with the number of training rounds t: λ t = 0.1×(1 + t / T);

[0162] (initial value 0.1, T is the total number of training rounds);

[0163] θt: the angle between the classification task gradient and the detection task gradient (unit: radian);

[0164] Lcls: classification loss function (such as cross-entropy);

[0165] Ldet: detection loss function (such as SmoothL1).

[0166] Through the dynamic task weighted loss, the weight allocation of multiple tasks (classification, detection) can be adaptively adjusted, task conflicts can be alleviated, and the comprehensive performance of the model in complex scenarios can be improved.

[0167] Among them, the adaptive optical acquisition module includes:

[0168] a) A polarization-sensitive CMOS array that supports synchronous capture of four-way polarized light;

[0169] b) A dynamic aperture control system, whose state space is defined as a triple of ambient illumination, target motion vector, and scene depth distribution;

[0170] c) The multi-spectral fusion unit adopts a spectral domain attention mechanism to calculate the weights of each band: w λ = σ(MLP(FFT(I λ )));

[0171] and generates a fused image:

[0172]

[0173] FFT: Fast Fourier Transform, which converts the image to the frequency domain;

[0174] MLP: Multi-Layer Perceptron, which generates band weights;

[0175] λ0: the central wavelength of the target band (such as 550nm in the visible light band);

[0176] σ: Gaussian kernel bandwidth, controlling the fusion range (value range: 10 - 50 nm);

[0177] σ(·): Sigmoid function, normalizing the weights to [0, 1].

[0178] This adaptive optical acquisition module, based on the band fusion strategy of frequency domain analysis, effectively integrates the complementarity of multi - spectral information and enhances the distinguishability of target features in different spectral bands.

[0179] Among them, the calculation formula of the three - dimensional attention fusion module is:

[0180]

[0181] Among them, GAPd and GMPd represent the pooling operations in the channel (C), spatial (S), and depth (D) dimensions respectively, and Wd is the learnable dimension weight matrix;

[0182] GAPd: Global average pooling in the channel (C), spatial (S), and depth (D) dimensions;

[0183] GMPd: Global maximum pooling in the corresponding dimension;

[0184] Wd: Learnable dimension weight matrix (dimension 3×1×1×1);

[0185] dim(F): Number of channels of the feature map;

[0186] ☉: Element - wise multiplication.

[0187] This three - dimensional attention fusion module, through dynamic feature weighting across the channel, spatial, and depth dimensions, enhances the significant expression of key features and can suppress the interference of redundant information.

[0188] Among them, the multi - dimensional training optimization engine includes a data augmentation module based on Neural Radiance Field (NeRF), and its rendering equation is:

[0189]

[0190] Among them, Φ is the three - dimensional radiation field function, modeling the geometric and material properties of the scene, β is the differentiable rendering parameter (such as lighting, reflectivity), generating adversarial samples with real physical properties; θ, φ are the pitch angle and azimuth angle of the viewing angle (unit: radian); R is the differentiable renderer, generating images with physical properties.

[0191] This multi - dimensional training optimization engine can generate adversarial samples with physical authenticity and enhance the robustness of the model to complex scenes such as lighting changes and occlusions.

[0192] Among them, the progressive model quantization algorithm includes:

[0193] a) Channel importance score:

[0194] Let \(s_c\) be the channel importance score, where a larger value indicates a more important channel; \(w_c\) (i) is the weight parameter of the \(i\)-th channel;

[0195] b) Adaptive threshold pruning: When \(s\) c \(< \tau\) t \(\cdot \max(s)\), prune the channel. The threshold \(\tau\) t \(= \tau_0 \cdot (1 - t / T)\) decays with training, where \(\tau_0\) is the initial pruning threshold (0.01 - 0.05); \(t\) is the current training epoch, and \(T\) is the total number of epochs.

[0196] c) Quantization compensation fine-tuning: Perform weight distribution calibration based on KL divergence after each 8-bit quantization.

[0197] This progressive model quantization algorithm, through the collaborative optimization of channel pruning and mixed-precision quantization, significantly reduces the model's computational overhead while ensuring accuracy, and can be adapted for deployment on edge devices.

[0198] This deep learning-based image recognition system also includes a real-time performance adjustment module, and its inference accuracy dynamic control equation is:

[0199] \(k\) is the environmental complexity factor, where a larger value indicates a more aggressive adjustment (0.5 - 2.0); \(t_0\) is the reference time offset (system initialization parameter); \(P_{max}\) is the maximum inference accuracy of the model (e.g., 99%);

[0200] When the frame rate FPS < \(FPS_{min}\), automatically reduce the computational load to \(P_{target}\), where \(k\) is the environmental complexity factor.

[0201] The set real-time performance adjustment module can dynamically adjust the inference resource allocation according to the environmental complexity, balance real-time performance and accuracy, and ensure the stable operation of the system in dynamic scenarios.

[0202] As Figure 2 shown, this embodiment also provides an image recognition method for a deep learning-based image recognition system, including the following steps:

[0203] S1. Multi-modal image acquisition:

[0204] Synchronously capture four-way polarized light images through a polarization-sensitive CMOS array, and obtain spectral domain data of \(N\) bands using a multispectral sensor. Dynamically adjust the aperture value \(F\) and exposure time according to the ambient light intensity parameter to generate an original input image group Where:

[0205] θ ∈ {0°, 45°, 90°, 135°} represents the four-directional polarization light acquisition direction;

[0206] λ ∈ Λ represents the multi-spectral band set, Λ = {λ1, λ2,..., λ N};

[0207] is the polarization light image corresponding to the polarization direction θ;

[0208] I λ corresponding spectral image;

[0209] S2. Heterogeneous joint preprocessing:

[0210] a) Quantum noise reduction processing: Apply noise modeling based on the Schrödinger equation to the original image:

[0211]

[0212] where the kernel function K satisfies Simulate the quantum tunneling effect for noise suppression, where:

[0213] σ q : Standard deviation of quantum noise, used to control the noise suppression intensity;

[0214] m: Simulated noise particle mass (physical parameter, unit: kg);

[0215] Reduced Planck constant;

[0216] E: Total energy of noise particles (unit: J);

[0217] V(x, y): Two-dimensional space potential function, describing the local noise barrier distribution of the image;

[0218] b) Dual discriminator enhancement: Input the denoised image into the generator G to generate an enhanced image

[0219] I enh = G(I denoised ), where the loss function of G is:

[0220] where:

[0221] D1, D2: Local texture discriminator and global semantic discriminator respectively;

[0222] E: Mathematical expectation operation;

[0223] λ per : Perceptual loss weight coefficient (hyperparameter, value range [0, 1]);

[0224] φ(·): The feature extractor of VGG19, which is used to extract high-level semantic features of images;

[0225] The square of the L2 norm, which measures the feature difference between the generated image and the original image;

[0226] S3. Hierarchical Feature Extraction and Fusion

[0227] Extract multi-scale features through a hierarchical reconfigurable convolutional network, and adopt a parametric Swish activation function:

[0228] Apply a three-dimensional attention fusion module:

[0229] Among them:

[0230] (C, S, D): Represent the channel, spatial, and depth dimensions respectively;

[0231] Among them, GAPd and GMPd: Average pooling and maximum pooling operations for the corresponding dimensions;

[0232] Wd: Learnable dimension weight matrix; ⊙: Element-wise multiplication; σ: Sigmoid activation function;

[0233] S4. Multi-Dimensional Training Optimization

[0234] Use a dynamic task weighted loss function:

[0235]

[0236] Generate adversarial samples based on Neural Radiance Field (NeRF):

[0237]

[0238] S5. Progressive Model Quantization and Inference:

[0239] a) Channel pruning: Prune inefficient channels according to the importance score:

[0240] When s c < τ t · max(s), pruning is performed, and the threshold τ t = τ0·(1 - t / T);

[0241] b) Quantization calibration: After performing 8-bit quantization, adjust the weight distribution based on the KL divergence;

[0242] Among them, the temperature coefficient λt = 0.1×(1 + t / T), t is the current training epoch, and T is the total number of training epochs; S6. Real-Time Performance Dynamic Adjustment:

[0243] Dynamically adjust the inference accuracy according to the environmental complexity:

[0244]

[0245] When the frame rate FPS < FPSmin, reduce the computational amount to Ptarget;

[0246] S7. Cross-modal verification and output:

[0247] Adjust the prediction probability through the confidence calibration module:

[0248] Pcalibrated = σ(log(p) - μ·U(x));

[0249] Where U(x) is the uncertainty measure; μ is the uncertainty calibration coefficient; p represents the original prediction probability of the model for the input data x, and its value range is (0, 1), serving as the calibration reference value. By combining the uncertainty measure U(x) and the calibration coefficient μ, a more reliable confidence Pealibrated is obtained after adjustment.

[0250] Visualize the decision traceability and output the final recognition result.

[0251] The image recognition method of this deep learning-based image recognition system integrates quantum noise reduction, multi-modal fusion, and dynamic optimization processes, forming a closed-loop recognition system, which significantly improves the processing ability for low-quality inputs (such as high noise, low light).

[0252] In summary, the deep learning-based image recognition system provided in this embodiment has the following advantages:

[0253] 1. Environmental adaptability: Through the fusion of polarized light and multi-spectral data, and dynamic aperture control, the system can still capture high-quality images in extreme lighting (strong reflection, low illuminance) scenarios.

[0254] 2. Improved robustness: Quantum noise reduction and NeRF data enhancement technologies effectively suppress noise interference and enhance the generalization ability of the model to physical property changes.

[0255] 3. Efficient inference: Progressive quantization and resource-aware optimization enable the model to significantly reduce computational complexity while maintaining high accuracy, and are suitable for mobile and embedded devices.

[0256] 4. Multi-task collaboration: Dynamic task weighting and three-dimensional attention mechanism realize the collaborative optimization of tasks such as classification and detection, and improve the comprehensive recognition accuracy in complex scenarios.

[0257] 5. Trusted decision output: Confidence calibration and visualization traceability functions enhance the interpretability of results and support the application requirements of key decision-making scenarios (such as medical diagnosis, autonomous driving).

[0258] The innovations of the present invention are as follows:

[0259] 1. Multi-modal dynamic fusion: Polarized light and multi-spectral data complement each other, combined with adaptive optical adjustment to break through the physical limitations of a single sensor.

[0260] 2. Physical enhancement training: Generate training data that conforms to real physical laws through quantum noise modeling and NeRF rendering to improve the generalization ability of the model.

[0261] 3. Resource-accuracy balance: Dynamic pruning, quantization, and real-time adjustment mechanisms to achieve the optimal trade-off between computational efficiency and accuracy.

[0262] 4. Closed-loop optimization system: Full-process optimization from data acquisition to decision output to form an adaptive image recognition ecosystem.

[0263] The experimental data is as follows:

[0264] 1. Dual discriminator generative adversarial network

[0265] Experimental design: In the low-light image enhancement task, compare the dual discriminator GAN with the traditional single discriminator GAN.

[0266] Results:

[0267] PSNR: The dual discriminator GAN is improved by about 15%, and more details are retained.

[0268] SSIM: The structural similarity index is increased by 12%, and artifacts are significantly reduced.

[0269] Visual comparison: The enhanced image shows more natural performance in the edge and texture regions (such as Figure 3 ).

[0270] 2. Parameterized Swish activation function

[0271] Experimental design: Replace ReLU with the parameterized Swish on ResNet-50 and compare the training curves and classification accuracies.

[0272] Results:

[0273] Convergence speed: The training loss decreases 20% faster.

[0274] Top-1 accuracy: It is improved by about 1.2% on the ImageNet validation set.

[0275] Gradient stability: The gradient vanishing problem is significantly alleviated (such as Figure 4 ).

[0276] 3. Dynamic task weighted loss function

[0277] Experimental Design: Compare fixed weights with dynamic weighting strategies in a multi-task model (classification + detection). Results:

[0278] Classification accuracy: Improved by 3.5%, and the detection mAP improved by 2.8%.

[0279] Task conflict: Gradient direction consistency analysis shows a 40% reduction in conflict (e.g., Figure 5 ).

[0280] 4. Spectral domain attention fusion

[0281] Experimental Design: Compare spectral domain attention fusion with RGB fusion in the vegetation classification task.

[0282] Results:

[0283] Classification accuracy: Improved by 8% in the key area of the near-infrared band.

[0284] Feature visualization: The features of the fused image are more prominent in the chlorophyll distribution area (e.g., Figure 6 ).

[0285] 5. 3D attention fusion module

[0286] Experimental Design: Compare 3D attention with 2D attention models in complex scenarios (such as occlusion). Results:

[0287] Object recognition accuracy: Improved by 6.7%.

[0288] Heatmap analysis: The attention in the depth dimension effectively focuses on occluded targets (e.g., Figure 7 ).

[0289] 6. NeRF data augmentation Experimental Design: Compare the model robustness before and after augmentation on the adversarial attack test sets (FGSM, PGD). Results:

[0290] Adversarial accuracy: Improved by 25% under FGSM attack and 18% under PGD attack.

[0291] Lighting generalization: The misjudgment rate is reduced by 30% in extreme lighting scenarios (e.g., Figure 8 ).

[0292] 7. Progressive model quantization

[0293] Experimental Design: Test the model performance before and after quantization on Jetson Nano.

[0294] Results:

[0295] Inference speed: The latency is reduced by 65% after 8-bit quantization.

[0296] Accuracy loss: The Top-1 accuracy only drops by 0.4% (e.g.,Figure 9 )。

[0297] 8. Real-time performance adjustment

[0298] Experimental design: Test the frame rate and resource occupancy in a dynamic lighting environment.

[0299] Results:

[0300] Frame rate stability: The frame rate fluctuation is less than 5% when the lighting changes suddenly.

[0301] Resource occupancy: The GPU memory occupancy is reduced by 40% (such as Figure 10 )。

[0302] 9. Overall system verification

[0303] Experimental design: Compare with the traditional system on COCO and ImageNet-C (noise dataset).

[0304] Results:

[0305] COCO mAP: Increased by 4.2%.

[0306] ImageNet-C accuracy: Increased by 12% under noise interference (such as Figure 11 )。

[0307] The above are only the preferred embodiments of the present invention, and do not limit the implementation manners and protection scope of the present invention accordingly. For those skilled in the art, it should be realized that all equivalent replacements and obvious changes made by using the description and illustrations of the present invention should be included in the protection scope of the present invention.

Claims

1. An image recognition system based on deep learning, characterized in that, Including: An adaptive optical acquisition module, integrating a polarization filtering array and a multi-spectral sensor, dynamically adjusting the aperture parameters through deep reinforcement learning; A heterogeneous preprocessing pipeline, including non-local mean denoising for quantum noise modeling, enhancement by a dual-discriminator generative adversarial network, and a dynamic normalization unit; A hierarchical reconfigurable convolutional network, adopting a staged feature distillation structure, including separable convolutions, hybrid pooling layers, and a three-dimensional attention fusion module; A multi-dimensional training optimization engine, integrating meta-learning initialization, a dynamic task weighted loss function, and progressive model quantization; A cross-modal verification output interface, equipped with confidence calibration and visual decision traceability functions.

2. The image recognition system based on deep learning according to claim 1, wherein The dual-discriminator generative adversarial network includes: a) The local texture discriminator uses 5-layer dilated convolutions to extract fine-grained features; b) The global semantic discriminator integrates a pyramid pooling module, with multi-scale pooling kernel sizes of {1, 3, 6, 8}; c) At the skip connections of the generator, a channel-spatial dual attention gate is embedded, and its gate weight calculation formula is:

3. The image recognition system based on deep learning according to claim 1, characterized in that, The hierarchical reconfigurable convolutional network adopts a parametric Swish activation function:

4. The image recognition system based on deep learning according to claim 1, characterized in that, The calculation formula of the dynamic task weighted loss function is:

5. The image recognition system based on deep learning according to claim 1, characterized in that, The adaptive optical acquisition module includes: a) A polarization-sensitive CMOS array, supporting synchronous capture of four-way polarized light; b) A dynamic aperture control system, whose state space is defined as a triple of ambient illumination, target motion vector, and scene depth distribution; c) The multi-spectral fusion unit uses a spectral domain attention mechanism to calculate the weights of each band: w λ = σ(MLP(FFT(I λ )); And generates a fused image:

6. The image recognition system based on deep learning according to claim 1, characterized in that, The calculation formula of the three-dimensional attention fusion module is:

7. The image recognition system based on deep learning according to claim 1, wherein, The multi-dimensional training optimization engine includes a data augmentation module based on neural radiance fields, and its rendering equation is:

8. The image recognition system based on deep learning according to claim 1, wherein The progressive model quantization algorithm includes: a) Channel importance scoring: b) Adaptive threshold pruning: Channels are pruned when sc < τt·max(s), and the threshold τt = τ0·(1 - t / T) decays with training; c) Quantization compensation fine-tuning: After each 8-bit quantization, perform weight distribution calibration based on KL divergence.

9. The image recognition system based on deep learning according to claim 1, characterized in that, It also includes a real-time performance adjustment module, and its inference accuracy dynamic control equation is:

10. The image recognition method of the deep learning-based image recognition system according to any one of claims 1-9, characterized in that, Including the following steps: S1. Multi-modal image acquisition: Synchronously capture four-way polarized light images through a polarization-sensitive CMOS array, obtain spectral domain data of N bands using a multispectral sensor, dynamically adjust the aperture value F and exposure time according to the ambient light intensity parameter, and generate a group of original input images S2. Heterogeneous joint preprocessing: a) Quantum noise reduction processing: Apply noise modeling based on the Schrödinger equation to the original image: where the kernel function K satisfies simulate the quantum tunneling effect for noise suppression; b) Dual-discriminator enhancement: Input the denoised image into the generator G to generate an enhanced image Ienh = G(Idenoised), where the loss function of G is: S3. Hierarchical feature extraction and fusion Extract multi-scale features through a hierarchical reconfigurable convolutional network and adopt a parametric Swish activation function: Apply the three-dimensional attention fusion module: S4. Multi-dimensional training optimization: a) Dynamic task weighting: Allocate weights according to the consistency of the gradient directions of classification and detection tasks: b) NeRF data augmentation: Generate physically realistic adversarial samples using neural radiance fields: S5. Progressive model quantization and inference: a) Channel pruning: Prune inefficient channels according to importance scores: Pruning is performed when sc < τt·max(s), where the threshold τt = τ0·(1 - t / T); b) Quantization calibration: After performing 8-bit quantization, adjust the weight distribution based on KL divergence; S6. Real-time performance dynamic adjustment: Dynamically adjust the inference accuracy according to the environmental complexity: When the frame rate FPS < FPSmin, reduce the computational load to Ptarget; S7. Cross-modal verification and output: Adjust the prediction probability through the confidence calibration module: Pcalibrated = σ(log(p) - μ·U(x)); Visualize the decision traceability and output the final recognition result.

Images