Eff-YOLOv7:An Improved Algorithm for Target Detection of Helmets and Vests in YOLOv7
In order to solve the problems of missed and poor detection of small objects in the ex-isting algorithms for safety helmet and vest detection in construction site scenes,this paper pro-poses an algorithm based on the improved YOLOv7 for safety helmet and vest detection,deno-ted as Eff-YOLOv7.Firstly,the proposed algorithm utilizes EfficientViT Module in the feature extraction stage to strengthen information extraction of the image;Secondly,in the feature fu-sion stage,a feature fusion extraction block is proposed to intergrate the global features with the local information,so that the intermediate feature maps can be better combined with the contex-tual information;Finally,the ICIoU Loss is proposed to calculate the positional deviation be-tween the predicted boxes and the ground-truth in a more fine-grained way.In order to verify the effectiveness of the proposed algorithm,based on the benchmark MS COCO dataset and the sel f-constructed safety helmet and vest dataset,numerous experiments show that the Eff-YOLOv7 has a substantial improvement in several indexes,such as mAP0.5 and mAP0.5∶0.95,on both datasets compared to the YOLOv7,which provides a new method for high-precision safety helmet and vest detection task.
YOLOv7Object DetectionICIoUSafety helmet detectionSafety Vest detection
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