Single-target detection of Oncomelania hupensis based on improved YOLOv5s - Fang, Juanyan; Meng, Jinbao; Liu, Xiaosong; Li, Yan; Qi, Ping; Wei, Changcheng (Frontiers in Bioengineering and Biotechnology, 2022)
To address the issues of low detection accuracy and poor results caused by small Oncomelania hupensis data samples and small target sizes. This article proposes the O. hupensis snails detection algorithm, the YOLOv5s-ECA-vfnet based on improved the YOLOv5s. Using the YOLOv5s as the base target detection model and optimizing the loss function to improve target learning ability for specific regions. The experimental results show that the snail detection method of the YOLOv5s-ECA-vfnet, the precision (P), the recall (R) and the mean Average Precision (mAP) of the algorithm are improved by 1.3 %, 1.26% and 0.87%. It shows that the algorithm has a good effect on snail detection. The algorithm is capable of accurately and rapidly identifying O. hupensis snails in a variety of lighting conditions, sizes, and densities. Further provide a new technology for precise and intelligent investigation of O. hupensiss snails in schistosomiasis prevention institutions.