Research on infrared scene target detection method based on improved YOLOv5s lightweight model
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.
infrared target detectionlightweight modelYOLOv5sCARAFEattention mechanismloss function
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