中图分类号：TP391.41 文献标志码：A DOI: 10.16157/j.issn.0258-7998.244869
中文引用格式： 廖辰津. 海洋环境下基于增强YOLOv7的垃圾目标检测[J]. 电子技术应用，2024，50(6)：66-70.
英文引用格式： Liao Chenjin. Garbage object detection based on enhanced YOLOv7 in marine environment[J]. Application of Electronic Technique，2024，50(6)：66-70.

Garbage object detection based on enhanced YOLOv7 in marine environment

Abstract： To address the issue of low model accuracy in practical applications of marine debris identification, this paper proposes an improved garbage classification algorithm based on optimized YOLOv7. In the image enhancement part, a probabilistic UIE framework is introduced to reduce the loss of feature information by incorporating eSE attention. In the loss function part, a two-layer attention mechanism is added to the IoU loss function to enhance the model’s generalization ability when combined with the EIoU loss function. The proposed algorithm is applied to marine debris detection tasks and evaluated on benchmark datasets. The average precision on the YOLOTrashCan datasets achieves 69.5% and 63.5%, respectively, representing a 6% and 1.6% improvement compared to the YOLOv7 algorithm. Overall experimental results demonstrate that the algorithm constructed in this paper effectively enhances the accuracy of marine debris detection.

Key words : EUIE；eSE attention；marine debris detection