Abstract
This paper proposes an anchor-free wheat ear detection method using ObjectBox with attention. First, on base of the backbone of ObjectBox, convolutional block attention module is used to improve the connection of each feature in the channel and space and enhance the feature extraction ability of the network. Second, in the neck part, ConvNeXtBlock is used to better fuse or extract the feature map given by the backbone. Last, the non-maximum suppression algorithm is improved to remove the center redundant detection box. The experimental results on the public global wheat head detection dataset show that the proposed method has an mean Average Precision (mAP) of 96.0%, an Precision of 94.5%, an Recall of 92.2% and \(F_{1}\) score of 93.3%. Compared with the original ObjectBox model, the improvement for mAP, Precision, Recall and \(F_{1}\) score is 2.0%, 1.3%, 2.9% and 2.1%, respectively. Compared with other existing wheat ear detection methods, it has higher detection accuracy.
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Data Availability
The GWHD dataset is publicly available at http://www.global-wheat.com/.
References
Timmer, C.P.: Food security in Asia and the Pacific: the rapidly changing role of rice. Asia Pac. Policy Stud. 1(1), 73–90 (2014)
Slafer, G.A., Savin, R., Sadras, V.O.: Coarse and fine regulation of wheat yield components in response to genotype and environment. Field Crop Res. 157, 71–83 (2014)
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. 39(6), 1137–1149 (2016)
Redmon, J., Farhadi, A.: Yolov3: An incremental improvement (2018). arXiv:1804.02767
Glenn: ultralytics/yolov5: v6.0 https://github.com/ultralytics/yolov5 (2020)
Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., & Tian, Q.: CenterNet++ for object detection (2022). arXiv:2204.08394
Zand, M., Etemad, A., Greenspan, M.: ObjectBox: from centers to boxes for anchor-free object detection. In: ECCV, pp. 390–406. Springer, Cham (2022)
Hasan, M.M., Chopin, J.P., Laga, H., et al.: Detection and analysis of wheat spikes using convolutional neural networks. Plant Methods 14(1), 1–13 (2018)
Madec, S., Jin, X., Lu, H., et al.: Ear density estimation from high resolution RGB imagery using deep learning technique. Agric. For. Meteorol. 264, 225–234 (2019)
Sun, J., Yang, K., Chen, C., et al.: Wheat head counting in the wild by an augmented feature pyramid networks-based convolutional neural network. Comput. Electron. Agric. 193, 106705 (2022)
Bhagat, S., Kokare, M., Haswani, V., et al.: WheatNet-lite: a novel light weight network for wheat head detection. In: CVPR, pp. 1332–1341. IEEE (2021)
Zhao, J., Yan, J., Xue, T., et al.: A deep learning method for oriented and small wheat spike detection (OSWSDet) in UAV images. Comput. Electron. Agr. 198, 107087 (2022)
Zhao, J., Zhang, X., Yan, J., et al.: A wheat spike detection method in UAV images based on improved YOLOv5. Remote Sens. 13(16), 3095 (2021)
Woo, S., Park, J., Lee, J.Y., & Kweon, I.S.: Cbam: Convolutional block attention module. In: ECCV, pp. 3–19 (2018)
Li, R., Wu, Y.: Improved YOLO v5 wheat ear detection algorithm based on attention mechanism. Electronics 11(11), 1673 (2022)
Lin, T.Y., Maire, M., Belongie, S., et al.: Microsoft coco: Common objects in context. In: ECCV, pp. 740–755. Springer, Cham (2014)
Liu, Z., Mao, H., Wu, C.Y., et al.: A convnet for the 2020s. In: CVPR, pp. 11976–11986. IEEE (2022)
David, E., Madec, S., Sadeghi-Tehran, P., et al.: Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high-resolution RGB-labelled images to develop and benchmark wheat head detection methods. Plant Phenomics 2020 (2020)
Elfwing, S., Uchibe, E., Doya, K.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Netw. 107, 3–11 (2018)
Liu, Z., Lin, Y., Cao, Y., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: CVPR, pp. 10012–10022. IEEE (2021)
Hendrycks, D., Gimpel, K.: Bridging nonlinearities and stochastic regularizers with gaussian error linear units. In: CoRR (2016). arXiv:1606.08415
Rothe, R., Guillaumin, M., & Gool, L.V.: Non-maximum suppression for object detection by passing messages between windows. In: ACCV, pp. 290–306. Springer, Cham (2014)
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MW and KS completed the experiment and wrote the manuscript. AG prepared the figures and tables. All authors reviewed the manuscript.
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Wang, M., Sun, K. & Guo, A. Wheat ear detection using anchor-free ObjectBox model with attention mechanism. SIViP 17, 3425–3432 (2023). https://doi.org/10.1007/s11760-023-02564-5
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DOI: https://doi.org/10.1007/s11760-023-02564-5