Object detection precision plays a critical role in computer vision tasks. Aiming at the precision problem available in the one-stage object detection model of the YOLOv5, this paper proposes an enhanced self-adaptive loss weight YOLOv5 model based on predicted bounding boxes in clusters presenting the individual targets in multi-resolution feature maps to optimize the multi-task loss. The enhanced model consists of the GT(ground true) target bounding box UID distributor, the GT target bounding box UID matcher, the bounding box position loss weight algorithm and the classification loss weight algorithm. The overall detection precision is improved by the enhancements of both position precision and classification precision in the YOLOv5. The experimental results present that compared with the YOLOv5.6, the mean average precision(mAP) is promoted relatively by 5.23% on average by the enhanced model which achieves the relative performance of 8.02% compared with the more complex model of YOLOv5x6.