Study on intelligent identification based on object detection algorithm of YOLOv5 for fish in waterway engineering
To promote the application of deep learning technology in the ecological impact assessment of waterway engineering and enhance the ecological and intelligent levels of inland waterway construction,36 typical fish species in the Yangtze River waterway engineering area were selected in this paper as target species.A fish target detection dataset for waterway engineering was established by adopting underwater image acquisition and manual annotation methods in both in-situ and indoor scenarios.Furthermore,a model based on the YOLOv5 object detection algorithm was trained by above dataset,which was subsequently tested and validated.The model test results indicated that the precision score of the training dataset was 0.933,the recall score was 0.98,and the F1 score at the balance point was 0.89,all close to 1,demonstrating effective training outcomes.The bounding box loss,object loss,and classification loss values of both the training and validation datasets approached zero,indicating a good fit between training and validation data.The confusion matrix diagram showed that different fish bodies could be distinguished from each other and accurately predicted into their respective categories.The overall mAP value of the validation dataset was 0.933,with a recall score of 0.98 and an F1 score of 0.89 at the balance point,indicating excellent recognition performance.Overall,the test results demonstrate that the YOLOv5-based object detection technology developed in this study achieves outstanding target detection and recognition effects for typical fish species in waterway engineering.
YOLOv5object detectionintelligent identification of fishwaterway engineeringdeep learningecological impact assessment
万方数据
维普
