Enhanced YOLOv5 Object Detection Algorithm for Accurate Detection of Adult <i>Rhynchophorus ferrugineus</i>
The red palm weevil (RPW, <i>Rhynchophorus ferrugineus</i>) is an invasive and highly destructive pest that poses a serious threat to palm plants. To improve the efficiency of adult RPWs’ management, an enhanced YOLOv5 object detection algorithm based on an attention mechanism is propose...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Insects |
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| Online Access: | https://www.mdpi.com/2075-4450/14/8/698 |
| _version_ | 1827729577023111168 |
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| author | Shuai Wu Jianping Wang Li Liu Danyang Chen Huimin Lu Chao Xu Rui Hao Zhao Li Qingxuan Wang |
| author_facet | Shuai Wu Jianping Wang Li Liu Danyang Chen Huimin Lu Chao Xu Rui Hao Zhao Li Qingxuan Wang |
| author_sort | Shuai Wu |
| collection | DOAJ |
| description | The red palm weevil (RPW, <i>Rhynchophorus ferrugineus</i>) is an invasive and highly destructive pest that poses a serious threat to palm plants. To improve the efficiency of adult RPWs’ management, an enhanced YOLOv5 object detection algorithm based on an attention mechanism is proposed in this paper. Firstly, the detection capabilities for small targets are enhanced by adding a convolutional layer to the backbone network of YOLOv5 and forming a quadruple down-sampling layer by splicing and down-sampling the convolutional layers. Secondly, the Squeeze-and-Excitation (SE) attention mechanism and Convolutional Block Attention Module (CBAM) attention mechanism are inserted directly before the SPPF structure to improve the feature extraction capability of the model for targets. Then, 2600 images of RPWs in different scenes and forms are collected and organized for data support. These images are divided into a training set, validation set and test set following a ratio of 7:2:1. Finally, an experiment is conducted, demonstrating that the enhanced YOLOv5 algorithm achieves an average precision of 90.1% (mAP@0.5) and a precision of 93.8% (P), which is a significant improvement compared with related models. In conclusion, the enhanced model brings a higher detection accuracy and real-time performance to the RPW-controlled pest pre-detection system, which helps us to take timely preventive and control measures to avoid serious pest infestation. It also provides scalability for other pest pre-detection systems; with the corresponding dataset and training, the algorithm can be adapted to the detection tasks of other pests, which in turn brings a wider range of applications in the field of monitoring and control of agricultural pests. |
| first_indexed | 2024-03-10T23:51:38Z |
| format | Article |
| id | doaj.art-a10366d09ebb4ef5bb180482219a0e92 |
| institution | Directory Open Access Journal |
| issn | 2075-4450 |
| language | English |
| last_indexed | 2024-03-10T23:51:38Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Insects |
| spelling | doaj.art-a10366d09ebb4ef5bb180482219a0e922023-11-19T01:36:46ZengMDPI AGInsects2075-44502023-08-0114869810.3390/insects14080698Enhanced YOLOv5 Object Detection Algorithm for Accurate Detection of Adult <i>Rhynchophorus ferrugineus</i>Shuai Wu0Jianping Wang1Li Liu2Danyang Chen3Huimin Lu4Chao Xu5Rui Hao6Zhao Li7Qingxuan Wang8School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaHainan Key Laboratory of Tropical Oil Crops Biology, Coconut Research Institute of Chinese Academy of Tropical Agricultural Sciences, Wenchang 571339, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaThe red palm weevil (RPW, <i>Rhynchophorus ferrugineus</i>) is an invasive and highly destructive pest that poses a serious threat to palm plants. To improve the efficiency of adult RPWs’ management, an enhanced YOLOv5 object detection algorithm based on an attention mechanism is proposed in this paper. Firstly, the detection capabilities for small targets are enhanced by adding a convolutional layer to the backbone network of YOLOv5 and forming a quadruple down-sampling layer by splicing and down-sampling the convolutional layers. Secondly, the Squeeze-and-Excitation (SE) attention mechanism and Convolutional Block Attention Module (CBAM) attention mechanism are inserted directly before the SPPF structure to improve the feature extraction capability of the model for targets. Then, 2600 images of RPWs in different scenes and forms are collected and organized for data support. These images are divided into a training set, validation set and test set following a ratio of 7:2:1. Finally, an experiment is conducted, demonstrating that the enhanced YOLOv5 algorithm achieves an average precision of 90.1% (mAP@0.5) and a precision of 93.8% (P), which is a significant improvement compared with related models. In conclusion, the enhanced model brings a higher detection accuracy and real-time performance to the RPW-controlled pest pre-detection system, which helps us to take timely preventive and control measures to avoid serious pest infestation. It also provides scalability for other pest pre-detection systems; with the corresponding dataset and training, the algorithm can be adapted to the detection tasks of other pests, which in turn brings a wider range of applications in the field of monitoring and control of agricultural pests.https://www.mdpi.com/2075-4450/14/8/698red palm weevilYOLOv5attention mechanismdetection |
| spellingShingle | Shuai Wu Jianping Wang Li Liu Danyang Chen Huimin Lu Chao Xu Rui Hao Zhao Li Qingxuan Wang Enhanced YOLOv5 Object Detection Algorithm for Accurate Detection of Adult <i>Rhynchophorus ferrugineus</i> Insects red palm weevil YOLOv5 attention mechanism detection |
| title | Enhanced YOLOv5 Object Detection Algorithm for Accurate Detection of Adult <i>Rhynchophorus ferrugineus</i> |
| title_full | Enhanced YOLOv5 Object Detection Algorithm for Accurate Detection of Adult <i>Rhynchophorus ferrugineus</i> |
| title_fullStr | Enhanced YOLOv5 Object Detection Algorithm for Accurate Detection of Adult <i>Rhynchophorus ferrugineus</i> |
| title_full_unstemmed | Enhanced YOLOv5 Object Detection Algorithm for Accurate Detection of Adult <i>Rhynchophorus ferrugineus</i> |
| title_short | Enhanced YOLOv5 Object Detection Algorithm for Accurate Detection of Adult <i>Rhynchophorus ferrugineus</i> |
| title_sort | enhanced yolov5 object detection algorithm for accurate detection of adult i rhynchophorus ferrugineus i |
| topic | red palm weevil YOLOv5 attention mechanism detection |
| url | https://www.mdpi.com/2075-4450/14/8/698 |
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