Improvement of YOLO v8 Segmentation Algorithm and Its Study in the Identification of Hazards in Plateau Pika
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 scien...
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MDPI AG
2024-11-01
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| author | Yaosheng Han Yunpeng Jin Chunmei Li Xiangjie Huang |
| author_facet | Yaosheng Han Yunpeng Jin Chunmei Li Xiangjie Huang |
| author_sort | Yaosheng Han |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b15587cb1d304033be6d76e5fc2fc70d |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-b15587cb1d304033be6d76e5fc2fc70d2025-08-20T02:50:13ZengMDPI AGApplied Sciences2076-34172024-11-0114231108810.3390/app142311088Improvement of YOLO v8 Segmentation Algorithm and Its Study in the Identification of Hazards in Plateau PikaYaosheng Han0Yunpeng Jin1Chunmei Li2Xiangjie Huang3College of Computer Technology and Applications, Qinghai University, Xining 810016, ChinaCollege of Computer Technology and Applications, Qinghai University, Xining 810016, ChinaCollege of Computer Technology and Applications, Qinghai University, Xining 810016, ChinaCollege of Computer Technology and Applications, Qinghai University, Xining 810016, ChinaRodent 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.https://www.mdpi.com/2076-3417/14/23/11088grass degradationplateau pikaYOLO v8attention mechanisminstance segmentation |
| spellingShingle | Yaosheng Han Yunpeng Jin Chunmei Li Xiangjie Huang Improvement of YOLO v8 Segmentation Algorithm and Its Study in the Identification of Hazards in Plateau Pika Applied Sciences grass degradation plateau pika YOLO v8 attention mechanism instance segmentation |
| title | Improvement of YOLO v8 Segmentation Algorithm and Its Study in the Identification of Hazards in Plateau Pika |
| title_full | Improvement of YOLO v8 Segmentation Algorithm and Its Study in the Identification of Hazards in Plateau Pika |
| title_fullStr | Improvement of YOLO v8 Segmentation Algorithm and Its Study in the Identification of Hazards in Plateau Pika |
| title_full_unstemmed | Improvement of YOLO v8 Segmentation Algorithm and Its Study in the Identification of Hazards in Plateau Pika |
| title_short | Improvement of YOLO v8 Segmentation Algorithm and Its Study in the Identification of Hazards in Plateau Pika |
| title_sort | improvement of yolo v8 segmentation algorithm and its study in the identification of hazards in plateau pika |
| topic | grass degradation plateau pika YOLO v8 attention mechanism instance segmentation |
| url | https://www.mdpi.com/2076-3417/14/23/11088 |
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