7results about How to "Improve target recognition ability" patented technology

The application discloses a kind of intelligent park target detection methods and systems for incomplete image.The method comprises: inputting first image into teacher model, obtaining teacher model target detection result, teacher model intermediate feature;Mask image corresponding to the first image is input into student model, and teacher model target detection result, student model intermediate feature are obtained;The teacher model intermediate feature, the student model intermediate feature are extracted respectively, and teacher model network feature map, student model network feature map are obtained;Detection task loss value is calculated;Global feature alignment loss value is calculated;The student model is optimized according to the detection task loss value, the global feature alignment loss value, the teacher model is updated, and new teacher model is obtained;Repeat training until reaching first preset condition, and obtain trained teacher model.The method has the characteristics of high target detection accuracy when facing incomplete image.

The invention relates to the field of antennas, and particularly discloses a dual-polarized antenna, which comprises a slot antenna layer for radiating a first polarized wave and a microstrip antenna layer for radiating a second polarized wave, wherein the slot antenna layer comprises a waveguide structure, the waveguide structure comprises a plurality of radiation units arranged along a first direction, and each radiation unit comprises a plurality of radiation slots extending along a second direction; the micro-strip antenna layer comprises a plurality of micro-strip oscillator units arranged along a first direction, and each micro-strip oscillator unit comprises a plurality of micro-strip oscillators arranged along a second direction; the microstrip antenna layer is arranged on the slot antenna layer, at least one microstrip array unit is arranged between the radiation units, and projections of the microstrip arrays and the radiation slots on the waveguide structure are not overlapped; the antenna is simple in structure, has the characteristics of low profile, miniaturization and light weight, can obtain the electromagnetic scattering characteristics of a target in different polarization directions, and is suitable for a high-frequency dual-polarized antenna.

The invention provides a plant station illegal behavior intelligent monitoring method and system based on an improved YOLO algorithm, and belongs to the technical field of industrial plant station safety management. The monitoring method comprises the steps of video stream acquisition and preprocessing, improved YOLO feature extraction and detection, specific illegal behavior identification, illegal behavior decision, alarm linkage, platform interaction and the like. The core improvement lies in that a lightweight backbone network is constructed by adopting GhostNet, and model parameters and calculation amount are reduced; a small target detection effect is optimized by newly adding a high-resolution detection head, introducing a BiFPN fusion mechanism and adopting a Focus loss and attention mechanism; color band channel enhancement, time sequence difference features and optical flow detection are fused, and flame / smoke recognition robustness is improved; and multiple types of illegal behavior recognition are integrated in the same model system, so that unified monitoring is realized. According to the method, the problems of insufficient model lightweight, unstable small target detection, unreliable fire identification, complex deployment and the like in the prior art are solved.

The invention discloses a marine mammal underwater acoustic target recognition method based on small sample deep learning, and relates to the field of underwater acoustic signal processing and recognition. In order to solve the problems of scarcity of samples, strong environmental interference and limited traditional single feature representation capability in underwater target identification, the method comprises the following steps: firstly, carrying out resampling and framing processing on underwater acoustic signals of marine mammals, and simulating a real marine environment based on a probabilistic random data enhancement strategy; three types of complementary acoustic features including a Gammatone frequency cepstrum coefficient, a time delay-Doppler feature and a wavelet time frequency feature are extracted and fused, and a multi-channel three-dimensional joint feature is constructed through standardization and early fusion; a ResNet-18 network is adopted for training, and dynamic learning rate scheduling and L2 regularization are introduced to enhance the generalization ability and robustness of the model. The recognition accuracy on a typical marine mammal audio data set is obviously better than that of a single feature and double feature fusion scheme, and the method is suitable for underwater acoustic target high-precision recognition under the small sample condition.

This invention discloses a method for obstacle recognition on expressways based on the cascaded use of Haar features and depth estimation features. The method obtains depth features from road images from depth data and performs shadow segmentation on the acquired road images using these features to obtain segmented images. Haar features and depth estimation features are extracted from the segmented images and subjected to parallel matching processing. A similarity-based attention mechanism is introduced to fuse the matching results of the Haar features and depth estimation features, resulting in a fused feature vector. The fused feature vector is then thresholded to obtain a depth estimation result. Based on the depth estimation result, a 3D model of the obstacle is reconstructed, and GPS is used to achieve obstacle recognition on expressways. This invention effectively solves the problems of low obstacle recognition accuracy due to illumination effects and poor target recognition capability for dynamic objects in road obstacle target detection.

The invention discloses a smart park target detection method and system for incomplete images. The method comprises the following steps: inputting a first image into a teacher model to obtain a teacher model target detection result and teacher model intermediate features; inputting the mask image corresponding to the first image into a student model to obtain a student model target detection result and student model intermediate features; respectively extracting the teacher model intermediate features and the student model intermediate features to obtain a teacher model network feature map and a student model network feature map; calculating to obtain a detection task loss value; calculating to obtain a global feature alignment loss value; optimizing the student model according to the detection task loss value and the global feature alignment loss value, and updating the teacher model to obtain a new teacher model; and the training is repeated until a first preset condition is reached, and a trained teacher model is obtained. The method has the characteristic of high target detection accuracy when facing the incomplete image.

The application discloses a sea surface target tracking method based on visual guidance radar under high sea conditions, and the method comprises the following steps: collecting millimeter wave radar point cloud, image, positioning, direction and speed data synchronously when the unmanned ship sails; using a YOLOv8 model optimized by slideloss weighting training and model distillation to detect the image target and obtain a target detection frame; preprocessing the radar point cloud; performing point cloud proposal and classification based on the image detection frame, respectively generating a clustering cluster through DBSCAN clustering, and fusing visual angle information of the proposed cluster; converting the clustering cluster to a world coordinate system, realizing double matching with a historical target through a Hungarian algorithm, initializing an unmatched target, and updating the state of a matched target and predicting the future position of the matched target. The application fills the short-distance perception blind area, significantly improves the precision and stability of sea surface target tracking under high sea conditions, and provides reliable protection for safe sailing of the unmanned ship.