关键词: 目标检测；YOLOv5s；注意力机制；损失函数；车载红外图像

Abstract: A target detection network based on the improvement of YOLOv5s is proposed to solve the problem of low detection efficiency due to the shortcomings of vehicle-mounted infrared image, such as inconspicuous performance of detail information, low contrast, and poor imaging effect. Firstly, a dynamic detection head based on an attention mechanism is added to the head network, with an attention mechanism between feature layers for scale perception, an attention mechanism between spatial positions for spatial perception, and an attention mechanism within the output channel for task perception, which makes the network focus more on detecting the foreground target associated with the task, and improves the expressive ability of the model target detection head. Then the CIOU bounding box loss function is replaced by MPDIOU during training to improve the localization accuracy and efficiency of the model. Finally, the lightweight network FasterNet is added to the C3 module in the end of the neck network to improve the real-time performance of the model. The experimental results show that the improved network model improves the mAP by 2.1% compared with the original network model before the improvement, and the weight size of the model is almost unchanged. It meets the requirements of small size and real-time, and is suitable for in-vehicle embedded systems.

Key words: Target detection; YOLOv5s; Attention mechanism; Loss function; Vehicle-mounted infrared image