Lightweight real-time detection method for infrared dim small target
By constructing a lightweight Mobilenetv3-Unet network and optimizing the convolution kernel and loss function, the problem of low detection efficiency of infrared weak target detection in complex scenes is solved, and efficient detection is achieved on resource-constrained devices.
CN115223026BActive Publication Date: 2026-05-12GUANGZHOU INSTITUTE OF TECHNOLOY XIDIAN UNIVERSITY
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Patent Information
- Application Number
- CN202210896509.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-07-28
- Publication Date
- 2026-05-12
- Estimated Expiration
- 2042-07-28
AI Technical Summary
Technical Problem
Existing infrared weak target detection algorithms have low detection efficiency in complex scenarios and are difficult to deploy on resource-constrained edge devices. The large computational load of the network model leads to low detection accuracy.
Method used
A lightweight network based on Mobilenetv3-Unet is constructed. Through a feature extraction backbone network and a feature aggregation network, the convolution kernel size and downsampling times are optimized. The model is trained by combining BCE and DICE loss functions to achieve real-time detection of infrared weak targets.
Benefits of technology
提高了红外弱小目标的检测精度和适应性，降低了计算资源需求，使得检测算法能够在资源受限的边缘设备上运行。
✦ Generated by Eureka AI based on patent content.
Abstract
The present application relates to the technical field of image recognition, in particular to a lightweight infrared small target real-time detection method, the detection method comprising the following steps: step one, constructing an infrared small target data set: obtaining the infrared small target and selecting and labeling it, step two, constructing an infrared small target detection network based on Mobilenetv3-Unet, step three, training the Mobilenetv3-Unet model using the infrared small target data set in step one to obtain the trained Mobilenetv3-Unet model, step four, using the trained Mobilenetv3-Unet model to detect the infrared small target of the input picture, the Mobilenetv3-Unet model in step two comprising a feature extraction backbone network and a feature aggregation network, wherein the feature extraction backbone network is an optimized Mobilenetv3 model, the present application not only improves the detection capability of the infrared small target in a complex scene, but also uses fewer computing resources, so that the small target detection algorithm can be easily deployed on resource-limited edge devices.
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