Improvement of YOLO v8 Segmentation Algorithm and Its Study in the Identification of Hazards in Plateau Pika - Yaosheng Han; Yunpeng Jin; Chunmei Li; Xiangjie Huang (Applied Sciences)
Rodent infestation has become one of the important factors in grassland degradation on the Qinghai–Tibet Plateau, one of the hindrances to ecological and environmental protection, and a threat to the balance and development of the ecosystem in the Sanjiangyuan region. Based on the need for the scientific planning for ecological protection, this paper designs a method for detecting rodent infestation in plateau scenarios. Firstly, data were collected and annotated, and a dataset of plateau rodent distribution in the Qinghai region was constructed. The collected data include videos captured through drone-based field surveys, which were processed using OpenCV and annotated with LabelMe. The dataset is categorized into four specific types: ungobbled rat holes, gobbled rat holes, rocks, and cow dung. This categorization allows the model to effectively differentiate between rodent-related features and other environmental elements, which is crucial for the segmentation task. Secondly, the latest segmentation algorithm provided by YOLO v8 is improved to design a segmentation algorithm that can accurately detect the distribution of rodent infestation in the plateau scene. The specific improvements are as follows: firstly, the Contextual Transformer module is introduced in YOLO v8 to improve the global modeling capability; secondly, the DRConv dynamic region-aware convolution is introduced in YOLO v8 to improve the convolutional representation capability; thirdly, the attention mechanism is incorporated in the backbone of YOLO v8 to enhance the feature extraction capability of the network capability. A comparison test with the original algorithm on the plateau rodent distribution dataset showed that the new algorithm improved the detection accuracy from 77.9% to 82.74% and MIoU from 67.65% to 72.69% on the plateau rodent distribution dataset. The accuracy of the evaluation of plateau rodent damage levels has been greatly improved.