Research on a Target Detection Algorithm for Common Pests Based on an Improved YOLOv7-Tiny Model
In agriculture and forestry, pest detection is critical for increasing crop yields and reducing economic losses. However, traditional deep learning models face challenges in resource-constrained environments, such as insufficient accuracy, slow inference speed, and large model sizes, which hinder th...
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MDPI AG
2024-12-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/14/12/3068 |
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| author | He Gong Xiaodan Ma Ying Guo |
| author_facet | He Gong Xiaodan Ma Ying Guo |
| author_sort | He Gong |
| collection | DOAJ |
| description | In agriculture and forestry, pest detection is critical for increasing crop yields and reducing economic losses. However, traditional deep learning models face challenges in resource-constrained environments, such as insufficient accuracy, slow inference speed, and large model sizes, which hinder their practical application. To address these issues, this study proposes an improved YOLOv7-tiny model designed to deliver efficient, accurate, and lightweight pest detection solutions. The main improvements are as follows: 1. Lightweight Network Design: The backbone network is optimized by integrating GhostNet and Dynamic Region-Aware Convolution (DRConv) to enhance computational efficiency. 2. Feature Sharing Enhancement: The introduction of a Cross-layer Feature Sharing Network (CotNet Transformer) strengthens feature fusion and extraction capabilities. 3. Activation Function Optimization: The traditional ReLU activation function is replaced with the Gaussian Error Linear Unit (GELU) to improve nonlinear expression and classification performance. Experimental results demonstrate that the improved model surpasses YOLOv7-tiny in accuracy, inference speed, and model size, achieving a MAP@0.5 of 92.8%, reducing inference time to 4.0 milliseconds, and minimizing model size to just 4.8 MB. Additionally, compared to algorithms like Faster R-CNN, SSD, and RetinaNet, the improved model delivers superior detection performance. In conclusion, the improved YOLOv7-tiny provides an efficient and practical solution for intelligent pest detection in agriculture and forestry. |
| format | Article |
| id | doaj-art-06df951687754bdbb9f55f7729c1864a |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-06df951687754bdbb9f55f7729c1864a2024-12-27T14:04:53ZengMDPI AGAgronomy2073-43952024-12-011412306810.3390/agronomy14123068Research on a Target Detection Algorithm for Common Pests Based on an Improved YOLOv7-Tiny ModelHe Gong0Xiaodan Ma1Ying Guo2College of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaIn agriculture and forestry, pest detection is critical for increasing crop yields and reducing economic losses. However, traditional deep learning models face challenges in resource-constrained environments, such as insufficient accuracy, slow inference speed, and large model sizes, which hinder their practical application. To address these issues, this study proposes an improved YOLOv7-tiny model designed to deliver efficient, accurate, and lightweight pest detection solutions. The main improvements are as follows: 1. Lightweight Network Design: The backbone network is optimized by integrating GhostNet and Dynamic Region-Aware Convolution (DRConv) to enhance computational efficiency. 2. Feature Sharing Enhancement: The introduction of a Cross-layer Feature Sharing Network (CotNet Transformer) strengthens feature fusion and extraction capabilities. 3. Activation Function Optimization: The traditional ReLU activation function is replaced with the Gaussian Error Linear Unit (GELU) to improve nonlinear expression and classification performance. Experimental results demonstrate that the improved model surpasses YOLOv7-tiny in accuracy, inference speed, and model size, achieving a MAP@0.5 of 92.8%, reducing inference time to 4.0 milliseconds, and minimizing model size to just 4.8 MB. Additionally, compared to algorithms like Faster R-CNN, SSD, and RetinaNet, the improved model delivers superior detection performance. In conclusion, the improved YOLOv7-tiny provides an efficient and practical solution for intelligent pest detection in agriculture and forestry.https://www.mdpi.com/2073-4395/14/12/3068pestYOLOv7-tinyfeature extractiontarget detectiondeep learning |
| spellingShingle | He Gong Xiaodan Ma Ying Guo Research on a Target Detection Algorithm for Common Pests Based on an Improved YOLOv7-Tiny Model Agronomy pest YOLOv7-tiny feature extraction target detection deep learning |
| title | Research on a Target Detection Algorithm for Common Pests Based on an Improved YOLOv7-Tiny Model |
| title_full | Research on a Target Detection Algorithm for Common Pests Based on an Improved YOLOv7-Tiny Model |
| title_fullStr | Research on a Target Detection Algorithm for Common Pests Based on an Improved YOLOv7-Tiny Model |
| title_full_unstemmed | Research on a Target Detection Algorithm for Common Pests Based on an Improved YOLOv7-Tiny Model |
| title_short | Research on a Target Detection Algorithm for Common Pests Based on an Improved YOLOv7-Tiny Model |
| title_sort | research on a target detection algorithm for common pests based on an improved yolov7 tiny model |
| topic | pest YOLOv7-tiny feature extraction target detection deep learning |
| url | https://www.mdpi.com/2073-4395/14/12/3068 |
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