计算机工程与科学2025，Vol.47Issue(4)：695-705,11.DOI:10.3969/j.issn.1007-130X.2025.04.013
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
摘要
Abstract
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 net-work to adapt to geometric variations of objects by enhancing spatial modeling capabilities.Spatial pyra-mid 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 F2 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 characteris-tics.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 en-vironment.关键词
YOLOv8/DCNv2/SPPFCSPC/上下文注意力机制/小目标检测头Key words
you only look once version 8(YOLOv8)/deformable ConvNets v2(DCNv2)/spatial pyra-mid pooling faster cross stage partial channel(SPPFCSPC)/contextual Transformer attention(CoTAt-tention)/small object detection head分类
信息技术与安全科学引用本文复制引用
梁佳杰,徐慧英,朱信忠,王舒梦,刘子洋,李琛..基于改进的YOLOv8n海洋动物目标检测算法:DPSC-YOLO[J].计算机工程与科学,2025,47(4):695-705,11.基金项目
国家自然科学基金(61976196) （61976196）
浙江省自然科学基金(LZ22F030003) （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) （202310345042）
