Region segmentation and localization method of water plants in crab pond based on improved YOLOv8n-seg

At present, the cleaning of water plants in crab pond is primarily relied on manual operations, which is characterized by high labor intensity and low efficiency. The automatic water plant cleaning boat can greatly reduce the labor intensity and improve the operation efficiency, and determining the water plant distribution in crab pond is the key basis for planning the efficient operation path of the cleaning boat. Addressing the challenges posed by the high similarity between water plants and shoreline vegetation, as observed in drone-captured images of the crab pond, which complicates the accurate distinction between them, a region segmentation and localization method for water plants in crab pond based on improved YOLOv8n-seg was proposed in this paper. The focus of the improved model lies in the reduction of the model size and the enhancement of the recall rate. Firstly, in order to diminish the model size, the lightweight HGNetv2 (hierarchical graph network) based on RT-DETR (real-time detection transformer) was used as the backbone feature extraction network. Secondly, the neck network was reconstructed based on the lightweight structure of Efficient Rep to reduce the number of parameters and enhance the multi-scale feature fusion ability of the mode. Finally, the SegNext attention mechanism was introduced in the feature extraction layer to enhance the sensitivity of the model to the water plant area. In order to eliminate the redundant regions generated by the model in the recognition process and further improve the segmentation accuracy, the binarization processing was used to optimize the segmentation results, and the image processing algorithm was combined to screen the area of the water plant area. After coordinate conversion, the precise longitude and latitude coordinates of water plant contour were obtained. The experimental results showed that the improved model exhibited a strong discriminatory and segmentation effect on water plants in crab ponds. The parameter number, calculation quantity and model size of the improved model were only 1.49 M, 8.4 GFLOPs, and 3.27 MB, respectively. Compared with the original YOLOv8n-seg model, the parameter number, calculation quantity and model size of the improved model were reduced by 54.3%, 30.6% and 49.7%, respectively. The model before and after improvement was deployed to the Jetson Orin Nano embedded AI computer for testing. It was found that the preprocessing speed of the improved model was almost the same as that of the YOLOv8n-seg model. The inference speed, post processing speed and segment speed were increased by 30.6%, 46.4% and 32.3%, respectively. In addition, the recall, precision, and mean average precision of the improved model achieved values of 91.5%, 89.3% and 95.6%, respectively. The improved model achieved the best balance of calculation, parameters, recall and detection accuracy comparing with YOLOv5s-seg, YOLOv8s-SwinTransformer, YOLOv8s-seg and YOLOv8n-GoldYOLO models. The coordinate conversion test showed that the minimum distance error of water plant location accuracy was 0.13m, the maximum distance error was 0.33 m, and the average distance error was 0.22 m, which verified that the improved model can meet the requirements of water plant region segmentation and localization in crab pond. The finding can provide an important reference for the automatic operation path planning of water plant cleaning boat.