A Novel Crop Pest Detection Model Based on YOLOv5
The damage caused by pests to crops results in reduced crop yield and compromised quality. Accurate and timely pest detection plays a crucial role in helping farmers to defend against and control pests. In this paper, a novel crop pest detection model named YOLOv5s-pest is proposed. Firstly, we desi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/14/2/275 |
| _version_ | 1827344399826157568 |
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| author | Wenji Yang Xiaoying Qiu |
| author_facet | Wenji Yang Xiaoying Qiu |
| author_sort | Wenji Yang |
| collection | DOAJ |
| description | The damage caused by pests to crops results in reduced crop yield and compromised quality. Accurate and timely pest detection plays a crucial role in helping farmers to defend against and control pests. In this paper, a novel crop pest detection model named YOLOv5s-pest is proposed. Firstly, we design a hybrid spatial pyramid pooling fast (HSPPF) module, which enhances the model’s capability to capture multi-scale receptive field information. Secondly, we design a new convolutional block attention module (NCBAM) that highlights key features, suppresses redundant features, and improves detection precision. Thirdly, the recursive gated convolution (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>g</mi></mrow><mrow><mn>3</mn></mrow></msup><mi>C</mi><mi>o</mi><mi>n</mi><mi>v</mi></mrow></semantics></math></inline-formula>) is introduced into the neck, which extends the potential of self-attention mechanism to explore feature representation to arbitrary-order space, enhances model capacity and detection capability. Finally, we replace the non-maximum suppression (NMS) in the post-processing part with Soft-NMS, which improves the missed problem of detection in crowded and dense scenes. The experimental results show that the mAP@0.5 (mean average precision at intersection over union (IoU) threshold of 0.5) of YOLOv5s-pest achieves 92.5% and the mAP@0.5:0.95 (mean average precision from IoU 0.5 to 0.95) achieves 72.6% on the IP16. Furthermore, we also validate our proposed method on other datasets, and the outcomes indicate that YOLOv5s-pest is also effective in other detection tasks. |
| first_indexed | 2024-03-07T22:46:40Z |
| format | Article |
| id | doaj.art-7f8045965fd3473da96bd85e046c5e12 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-07T22:46:40Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-7f8045965fd3473da96bd85e046c5e122024-02-23T15:03:47ZengMDPI AGAgriculture2077-04722024-02-0114227510.3390/agriculture14020275A Novel Crop Pest Detection Model Based on YOLOv5Wenji Yang0Xiaoying Qiu1Software College, Jiangxi Agricultural University, Nanchang 330045, ChinaSoftware College, Jiangxi Agricultural University, Nanchang 330045, ChinaThe damage caused by pests to crops results in reduced crop yield and compromised quality. Accurate and timely pest detection plays a crucial role in helping farmers to defend against and control pests. In this paper, a novel crop pest detection model named YOLOv5s-pest is proposed. Firstly, we design a hybrid spatial pyramid pooling fast (HSPPF) module, which enhances the model’s capability to capture multi-scale receptive field information. Secondly, we design a new convolutional block attention module (NCBAM) that highlights key features, suppresses redundant features, and improves detection precision. Thirdly, the recursive gated convolution (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>g</mi></mrow><mrow><mn>3</mn></mrow></msup><mi>C</mi><mi>o</mi><mi>n</mi><mi>v</mi></mrow></semantics></math></inline-formula>) is introduced into the neck, which extends the potential of self-attention mechanism to explore feature representation to arbitrary-order space, enhances model capacity and detection capability. Finally, we replace the non-maximum suppression (NMS) in the post-processing part with Soft-NMS, which improves the missed problem of detection in crowded and dense scenes. The experimental results show that the mAP@0.5 (mean average precision at intersection over union (IoU) threshold of 0.5) of YOLOv5s-pest achieves 92.5% and the mAP@0.5:0.95 (mean average precision from IoU 0.5 to 0.95) achieves 72.6% on the IP16. Furthermore, we also validate our proposed method on other datasets, and the outcomes indicate that YOLOv5s-pest is also effective in other detection tasks.https://www.mdpi.com/2077-0472/14/2/275pest detectionYOLOv5new convolutional block attention modulerecursive gated convolutionSoft-NMS |
| spellingShingle | Wenji Yang Xiaoying Qiu A Novel Crop Pest Detection Model Based on YOLOv5 Agriculture pest detection YOLOv5 new convolutional block attention module recursive gated convolution Soft-NMS |
| title | A Novel Crop Pest Detection Model Based on YOLOv5 |
| title_full | A Novel Crop Pest Detection Model Based on YOLOv5 |
| title_fullStr | A Novel Crop Pest Detection Model Based on YOLOv5 |
| title_full_unstemmed | A Novel Crop Pest Detection Model Based on YOLOv5 |
| title_short | A Novel Crop Pest Detection Model Based on YOLOv5 |
| title_sort | novel crop pest detection model based on yolov5 |
| topic | pest detection YOLOv5 new convolutional block attention module recursive gated convolution Soft-NMS |
| url | https://www.mdpi.com/2077-0472/14/2/275 |
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