CoT-YOLO水下目标检测算法
CoT-YOLO underwater target detection algorithm
苏佳 1冯康康 1梁奔 1侯卫民1
作者信息
- 1. 河北科技大学信息科学与工程学院,河北石家庄 050018
- 折叠
摘要
水下检测由于背景复杂、光线暗淡、目标遮挡重叠等问题导致检测精度较低,提出一种CoT-YOLO水下目标检测算法提高检测精度.使用YOLOv5s作为基础模型,构建注重上下文信息的卷积神经网络,充分利用特征信息,增强全局特征表达能力,解决模型漏检、误检问题;改用解耦头,加快收敛速度;增添新的检测层并重获先验框,增强模型对小目标的检测能力,提高水下小目标检测效果;采用EIoU损失函数提高目标边界框的定位与回归.实验结果表明,改进后算法精确度达到77.9％,相较于基线提升了 3.7％,mAP提升了 5.2％,验证了该方法的有效性.
Abstract
Underwater detection has low detection accuracy due to complex background,dim light and overlapping target occlu-sion.The CoT-YOLO underwater target detection algorithm was proposed to improve detection accuracy.Using YOLOv5s as the base model,a convolutional neural network focusing on contextual information was constructed to make full use of feature information and enhance global feature representation to solve the problem of missed and false detection of the model.The decou-pling head was used instead to speed up convergence.New detection layers were added and priori frames were regained to enhance the detection capability of the model for small targets and improve the detection effect of small underwater targets.The EIoU loss function was used to improve the target bounding box localization and regression.Experimental results show that the improved algorithm achieves 77.9％accuracy,a 3.7％improvement compared to the baseline,and a 5.2％improvement in mAP,verifying the effectiveness of the method.
关键词
目标检测/YOLOv5/卷积神经网络/特征信息/分类回归/解耦头/EIoU损失函数Key words
object detection/YOLOv5 algorithm/convolutional neural network/feature information/categorical regression/de-coupled head/EIoU loss function引用本文复制引用
基金项目
国家自然科学基金青年科学基金项目(62105093)
装备预研重点实验室基金项目(6142A010301)
2023年河北科技大学创新基金项目(XJCXZZSS202303)
出版年
2024
NETL
NSTL
万方数据