Research on infrared scene target detection method based on improved YOLOv5s lightweight model

DOI:	10.11809/bqzbgcxb2024.03.042
Keywords:	infrared target detection; lightweight model; YOLOv5s; CARAFE; attention mechanism; loss function
Abstract:	Infrared technology plays a crucial role in nighttime and covert operations. To address the issue of balancing the detection accuracy and lightweight design of infrared image detection, a lightweight target detection model called M Yolov5S is proposed for infrared scenes. This network model replaces the original CSP backbone network with an improved ShuffleBlock module.Additionally, it utilizes the lightweight up sampling operator CARAFE to replace the original up sampling module and incorporates SE attention mechanism into the C3 module to reduce redundant information, enhance feature distinctiveness, and representation capability. The loss function is redesigned, with E IoU as the new loss function, which accelerates model convergence. Experimental tests conducted on the FLIR public dataset show that the improved network model achieves an average detection accuracy of 73.0%, with only a 2.9 percentage point decrease compared to the baseline Yolov5 model. Furthermore, M YOLOv5S reduces the number of network parameters and theoretical computation by 40% and 39%, respectively, while improving the model’s inference speed by 52%, making it suitable for deployment on edge devices.
Published:	2024-03-28
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