基于改进的YOLOv8n海洋动物目标检测算法:DPSC-YOLO  

An improved marine animal object detection algorithm based on YOLOv8n:DPSC-YOLO

作　　者：梁佳杰 徐慧英[1] 朱信忠[1] 王舒梦 刘子洋 李琛 LIANG Jiajie;XU Huiying;ZHU Xinzhong;WANG Shumeng;LIU Ziyang;LI Chen(School of Computer Science and Technology(School of Aritficial Intelligence),Zhejiang Normal University,Jinhua 321004,China)

机构地区：[1]浙江师范大学计算机科学与技术学院(人工智能学院),浙江金华321004

出　　处：《计算机工程与科学》2025年第4期695-705,共11页Computer Engineering & Science

基　　金：国家自然科学基金(61976196);浙江省自然科学基金(LZ22F030003);国家级大学生创新创业训练计划项目创新训练重点项目(202310345042)。

摘　　要：在海洋复杂的环境中,由于图像拍摄模糊、背景复杂,导致基于深度学习的目标检测算法存在特征提取困难和目标漏检等问题,因此海洋目标检测算法需要更加高效且性能优越。为此提出了一种基于YOLOv8n改进的海洋动物目标检测算法:DPSC-YOLO。在主干网络中引入DCNv2模块,通过增强空间建模能力来适应对象的几何变化;在主干网络末端引入空间金字塔池化SPPFCSPC,在保持模型感知场不变的同时减少模型的计算量;在颈部网络增加F 2极小目标检测头,结合其余3个尺度,使用4个不同的感受野检测层提高小目标检测精度;在颈部网络的C2f模块中结合CoTAttention注意力机制更好地利用相邻键之间的上下文信息,并根据数据的特点动态调整注意力分配。实验结果表明,DPSC-YOLO目标检测算法与YOLOv8n相比mAP@0.5提升了1.1%,mAP@0.5:0.95提升了4.6%,同时仅有较少的参数量和计算量的增加,证明DPSC-YOLO更适合复杂海洋环境中的目标检测任务。In the complex marine environment,deep learning-based object detection algorithms face challenges such as difficulty in feature extraction and missed detection due to blurred images capture and complex backgrounds.Therefore,marine object detection algorithms need to be more efficient and superior in performance.To address this,an improved marine animal detection algorithm based on YOLOv8n,named DPSC-YOLO,is proposed.The DCNv2 module is introduced into the backbone network to adapt to geometric variations of objects by enhancing spatial modeling capabilities.Spatial pyramid pooling faster cross stage partial channel(SPPFCSPC)is added at the end of the backbone network to reduce computational complexity while maintaining the model's receptive field.An F 2 small object detection head is added to the neck network,combined with the other three scales,using four different receptive field detection layers to improve the accuracy of extremely small object detection.The CoT-Attention mechanism is integrated into the C2f module of the neck network to better utilize contextual information between adjacent keys and dynamically adjust attention allocation based on data characteristics.Experimental results show that DPSC-YOLO improves mAP@0.5 by 1.1% and mAP@0.5:0.95 by 4.6% compared to YOLOv8n,with only a slight increase in parameters and computational com-plexity.This proves that DPSC-YOLO is more suitable for object detection tasks in complex marine environment.

关 键 词：YOLOv8 DCNv2 SPPFCSPC 上下文注意力机制 小目标检测头 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]