Pest Region Detection in Complex Backgrounds via Contextual Information and Multi-Scale Mixed Attention Mechanism

In precision agriculture, effective monitoring of corn pest regions is crucial to developing early scientific prevention strategies and reducing yield losses. However, complex backgrounds and small objects in real farmland bring challenges to accurate detection. In this paper, we propose an improved model based on YOLOv4 that uses contextual information and attention mechanism. Firstly, a context priming module with simple architecture is designed, where effective features of different layers are fused as additional context features to augment pest region feature representation. Secondly, we propose a multi-scale mixed attention mechanism (MSMAM) with more focus on pest regions and reduction of noise interference. Finally, the mixed attention feature-fusion module (MAFF) with MSMAM as the kernel is applied to selectively fuse effective information from additional features of different scales and alleviate the inconsistencies in their fusion. Experimental results show that the improved model performs better in different growth cycles and backgrounds of corn, such as corn in vegetative 12th, the vegetative tasseling stage, and the overall dataset. Compared with the baseline model (YOLOv4), our model achieves better average precision (AP) by 6.23%, 6.08%, and 7.2%, respectively. In addition, several comparative experiments were conducted on datasets with different corn growth cycles and backgrounds, and the results verified the effectiveness and usability of the proposed method for such tasks, providing technical reference and theoretical research for the automatic identification and control of pests.

[1]  Xiushan Wang,et al.  Fast and accurate green pepper detection in complex backgrounds via an improved Yolov4-tiny model , 2021, Comput. Electron. Agric..

[2]  Lifa Fang,et al.  Using Channel and Network Layer Pruning Based on Deep Learning for Real-Time Detection of Ginger Images , 2021, Agriculture.

[3]  O. Erenstein,et al.  Estimating the global number and distribution of maize and wheat farms , 2021 .

[4]  Renjie Huang,et al.  A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies , 2021, Agriculture.

[5]  Jun Liu,et al.  Tomato Diseases and Pests Detection Based on Improved Yolo V3 Convolutional Neural Network , 2020, Frontiers in Plant Science.

[6]  Hong-Yuan Mark Liao,et al.  YOLOv4: Optimal Speed and Accuracy of Object Detection , 2020, ArXiv.

[7]  Po Yang,et al.  Fusing multi-scale context-aware information representation for automatic in-field pest detection and recognition , 2020, Comput. Electron. Agric..

[8]  Songtao Liu,et al.  Learning Spatial Fusion for Single-Shot Object Detection , 2019, ArXiv.

[9]  Qifu Wang,et al.  Method for pests detecting in stored grain based on spectral residual saliency edge detection , 2019, Grain & Oil Science and Technology.

[10]  En Li,et al.  Apple detection during different growth stages in orchards using the improved YOLO-V3 model , 2019, Comput. Electron. Agric..

[11]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[12]  Abhinav Gupta,et al.  Non-local Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[13]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Graham W. Taylor,et al.  Automatic moth detection from trap images for pest management , 2016, Comput. Electron. Agric..

[16]  Jeremy S. Smith,et al.  Image pattern classification for the identification of disease causing agents in plants , 2009 .

[17]  Rujing Wang,et al.  S-RPN: Sampling-balanced region proposal network for small crop pest detection , 2021, Comput. Electron. Agric..