Application analysis of the YOLOv5s-CBAM algorithm for the identification of eggs of Pomacea

Pomacea is an invasive species of major concern in China, which can have a negative impact on crop growth and the ecological environment. The timely acquisition of information on the distribution of eggs of Pomacea can help to prevent and control its invasion in advance. In order to improve the extraction of feature information from the Pomacea eggs in complex natural environments, this paper suggests a YOLOv5s-CBAM model for eggs of Pomacea recognition based on the YOLOv5s base network model and incorporating the CBAM（Convolutional Block Attention Module） attention mechanism module. The experimental results show that the incorporation of the CBAM module provides better recognition than the introduction of the CA and SE attention modules. The YOLOv5s-CBAM model, which incorporates CBAM, has a stronger recognition impact than the original YOLOv5s model and can, in some cases, overcome interference from elements like plant obscuration and reflection in the water. A 2.5 percentage point improvement over the original model, the mAP now stands at 83.8%. The method based on deep learning is feasible to identify the eggs of Pomacea in photographs obtained in complicated natural environment, which provides a fresh perspectives for the monitoring, prevention, and management of invasive species such as Pomacea.