Infrared security object detection based on improved YOLOv7
To address the problem of poor object detection due to low contrast and inconspicuous object contours in infrared images in security scenes,an improved YOLOv7-based infrared security object detection algorithm is pro-posed.The recursive gated convolution is used to improve the backbone network and enhance the ability to interact with higher-order information of the input image;the ELAN-S module is constructed using the Sim AM attention mech-anism to reduce the information loss rate while reducing the network parameters;the anchor box size is optimized using the K-means++clustering algorithm to improve the detection accuracy.Data enhancement and experiments are con-ducted on the InfiRay public dataset,and the results show that the algorithm proposed in this paper has an mAP value of 87.15％while maintaining a high detection speed,which is a significant improvement compared with the original YOLOv7 network and other mainstream algorithms,proving that the improved method is advanced and effective.
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