摘要
为解决真实养殖环境下,水下成像模糊、失真等导致鱼群检测准确率低的问题,提出一种融合视觉注意力机制SKNet(selective kernel networks)与YOLOv5(you only look once)的养殖鱼群检测方法(SK-YOLOv5模型),该方法首先采用UNet(convolutional networks for biomedical image segmentation)对图像进行预处理,得到清晰的鱼群图像,然后将SKNet融合到YOLOv5的Backbone端构成关注像素级信息的特征提取网络,加强对模糊鱼体的识别能力,并在水下模糊鱼群图像数据集上进行了消融试验和模型对比试验,以验证SK-YOLOv5的有效性。结果表明:在鱼群检测任务上,SK-YOLOv5的识别精确率和召回率分别达到了98.86%和96.64%,检测效果比YOLOv5分别提升了2.14%和2.29%,与目前检测准确率较高的水下目标检测模型XFishHmMp和FERNet相比,SK-YOLOv5取得了较好的检测效果,与XFishHmMp模型相比,识别精确率和召回率分别提升了5.39%和5.66%,与FERNet模型相比,识别精确率和召回率分别提升了3.59%和3.77%,实现了真实养殖环境下鱼群的准确检测。研究表明,融合SKNet与YOLOv5的养殖鱼群检测方法,有效地解决了水下模糊图像鱼群检测准确率低的问题,提升了养殖鱼群检测和识别的整体效果。
In order to solve the problem of low accuracy of fish detection caused by underwater imaging blur and distortion in actual aquaculture environment,a fish detection method(SK-YOLOv5)combining visual attention mechanism SKNet(selective kernel networks)and YOLOv5(you only look once)is proposed.In this method,UNet(convolutional networks for biomedical image segmentation)is firstly used to preprocess images to obtain clear fish images,and then SKNet is fused to Backbone end of YOLOv5 to form feature extraction network focusing on pixel-level information to strengthen the recognition ability of fuzzy fish.In this study,ablation test and model comparison test were carried out on underwater fuzzy fish swarming image data se to verify the effectiveness of SK-YOLOv5.The results showed that SK-YOLOv5 was effective in fish swarm detection task,and had recognition accuracy of 98.86%and recall rate of 96.64%,2.14%higher and 2.29%higher compared with YOLOv5,respectively.Compared with XFishHmMp and FERNet with the maximal detection accuracy underwater target detection model,SK-YOLOv5 had the best detection effect,5.39%higher in recognition accuracy,and 5.66%higher recall rate,and compared with FERNet,the recognition accuracy was improved by 3.59%and recall rate by 3.77%.The findings indicated that the fish detection of fusing SKNet and YOLOv5 can effectively enhence the identification ability of fuzzy fish,and improve the overall effect of fish detection and recognition.
作者
赵梦
于红
李海清
胥婧雯
程思奇
谷立帅
张鹏
韦思学
郑国伟
ZHAO Meng;YU Hong;LI Haiqing;XU Jingwen;CHENG Siqi;GU Lishuai;ZHANG Peng;WEI Sixue;ZHENG Guowei(College of Information Engineering, Liaoning Provincial Key Laboratory of Marine Information Technology, Dalian Ocean University, Dalian 116023, China;Key Laboratory of Environment Controlled Aquaculture (Dalian Ocean University), Ministry of Education, Dalian 116023, China)
出处
《大连海洋大学学报》
CAS
CSCD
北大核心
2022年第2期312-319,共8页
Journal of Dalian Ocean University
基金
辽宁省重点研发计划项目(2020JH2/10100043)
辽宁省科技重大专项(2020JH1/10200002)
辽宁省教育厅重点科研项目(LJKZ0729)
国家自然科学基金(31972846)。
