Adding GAM attention, a layer-2 feature fusion connection, and WIoUv3 loss to YOLOv8s raises trans-location mAP50 from 0.520 to 0.541 on the Caltech Camera Traps subset.
Improved YOLOv8 Detection Algorithm in Security Inspection Image
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abstract
Security inspection is the first line of defense to ensure the safety of people's lives and property, and intelligent security inspection is an inevitable trend in the future development of the security inspection industry. Aiming at the problems of overlapping detection objects, false detection of contraband, and missed detection in the process of X-ray image detection, an improved X-ray contraband detection algorithm CSS-YOLO based on YOLOv8s is proposed.
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Improving Generalization Performance of YOLOv8 for Camera Trap Object Detection
Adding GAM attention, a layer-2 feature fusion connection, and WIoUv3 loss to YOLOv8s raises trans-location mAP50 from 0.520 to 0.541 on the Caltech Camera Traps subset.