AUTHOR=Fu Gui , Han Lixiang , Huang Shaohan , Liu Liwen , Wang You , Zhu Xinyu TITLE=Research on infrared target detection based on neural network fusion JOURNAL=Frontiers in Physics VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2023.1181928 DOI=10.3389/fphy.2023.1181928 ISSN=2296-424X ABSTRACT=

Infrared technology can detect targets under special weather conditions, such as night, rain and fog. To improve the detection accuracy of vehicles, pedestrians and other targets in infrared images, an infrared target detection algorithm with fusion neural network is proposed. Firstly, we use Ghost convolution to replace the resunit unit of the convolution layer of the deep residual network layer in YOLOv5s, which can reduce the amount of parameters without losing accuracy. Then, the global channel attention (GCA) is added to the upper sampling layer, the detection accuracy of network is further improved by enhancing the characteristics of the overall goal. Also, the Channel Space Attention (CPA) space attention mechanism is added to the output end to obtain more accurate target location information. The infrared data set taken by the UAV is trained and tested. The accuracy rate of detection based on YOLOv5s and fusion neural network is 96.47%, the recall rate is 91.51%, and the F1 score is 94%, which is 7% higher than YOLOv5s. The results show that the target detection rate of infrared images is improved by proposed method, which has strong research value and broad application prospects.