Real-Time Detection of <i>Eichhornia crassipes</i> Based on Efficient YOLOV5

Abstract

The rapid propagation of Eichhornia crassipes has a threatening impact on the aquatic environment. For most small water areas with good ecology, daily manual monitoring and salvage require considerable financial and material resources. Unmanned boats have important practical significance for the automatic monitoring and cleaning Eichhornia crassipes. To ensure that the target can be accurately detected, we solve the problems that exist in the lightweight model algorithm, such as low accuracy and poor detection effect on targets with small or unclear characteristics. Taking YOLOV5m 6.0 version as the baseline model, given the computational limit of real-time detection, this paper proposes to use EfficientNet-Lite0 as the backbone, use the ELU function as the activation function, modify the pooling mode in SPPF, embed the SA attention mechanism, and add the RFB module in the feature fusion network to improve the feature extraction ability of the whole model. The dataset collected water hyacinth images from ponds and lakes in Guangxi, Yunnan, and the China Plant Image Library. The test results show that efficient YOLOV5 reached 87.6% mAP, which was 7.1% higher than that of YOLOV5s, and the average detection time was 62 FPS. The ablation experiment verifies the effectiveness of each module of efficient YOLOV5, and its detection accuracy and model parameters meet the real-time detection requirements of the Eichhornia crassipes unmanned cleaning boat.

Keywords

• <i>Eichhornia crassipes</i> detection
• YOLOV5
• deep learning
• EfficientNet