Fast and accurate detection of kiwifruits in the natural environment using improved YOLOv4
Abstract
Keywords: kiwifruits, fruit recognition, natural environments, YOLOv4
DOI: 10.25165/j.ijabe.20241705.7658
Citation: Wang J P, Xu L, Mei S, Hu H R, Zhou J L, Chen Q. Fast and accurate detection of kiwifruits in the natural environment using improved YOLOv4. Int J Agric & Biol Eng, 2024; 17(5): 222-230.
Keywords
Full Text:
PDFReferences
Xiao X, Li M. Fusion of data-driven model and mechanistic model for kiwifruit flesh firmness prediction. Computers and Electronics in Agriculture, 2022; 193: 106651.
Yang C, Lee W S, Gader P. Hyperspectral band selection for detecting different blueberry fruit maturity stages. Computers and Electronics in Agriculture, 2014; 109: 23–31.
Song Z Z, Zhou Z X, Wang W Q, Gao F F, Fu L S, Li R, et al. Canopy segmentation and wire reconstruction for kiwifruit robotic harvesting. Computers and. Electronics in Agriculture, 2021; 181: 105933.
Mu L T, Liu H Z, Cui Y J, Fu L S, Gejima Y. Mechanized technologies for scaffolding cultivation in the kiwifruit industry: A review. Information Processing in Agriculture, 2018; 5(4): 401–410.
Zhang Z, Igathinathane C, Li J, Cen H, Lu Y, Flores P. Technology progress in mechanical harvest of fresh market apples. Computers and Electronics in Agriculture, 2020; 175: 105606.
Jia W K, Zhang Z H, Shao W J, Hou S J, Ji Z, Liu G L, et al. FoveaMask: A fast and accurate deep learning model for green fruit instance segmentation. Computers and Electronics in Agriculture, 2021; 191: 106488.
Cui Y J, Su S, Wang X X, Tian Y F, Li P P, et al. Recognition and feature extraction of kiwifruit in natural environment based on machine vision. Transactions of CSAM, 2013; 44(5): 247–252. (in Chinese)
Tian K, Li J H, Zeng J F, Evans A, Zhang L N. Segmentation of tomato leaf images based on adaptive clustering number of K-means algorithm. Computers and Electronics in Agriculture, 2019; 165: 104962.
Maldonado W, Barbosa J C. Automatic green fruit counting in orange trees using digital images. Computers and Electronics in Agriculture, 2016; 127: 572–581.
Wiatowski T, Bölcskei H. A mathematical theory of deep convolutional neural networks for feature extraction. IEEE Transactions on Information Theory, 2018; 64(3): 1845–1866.
Majeed Y, Karkee M, Zhang Q. Estimating the trajectories of vine cordons in full foliage canopies for automated green shoot thinning in vineyards. Computers and Electronics in Agriculture, 2020; 176: 105671.
Zhou J, Fu X Q, Zhou S Q, Zhou J F, Ye H, Nguyen H T. Automated segmentation of soybean plants from 3D point cloud using machine learning. Computers and Electronics in Agriculture, 2019; 162: 143–153.
Xu C Y, Liu Y, Ding F L, Zhuang Z L. Recognition and grasping of disorderly stacked wood planks using a local image patch and point pair feature method. Sensors, 2020; 20(21): 6235.
Jin X J, Sun Y X, Yu J L, Chen Y. Weed recognition in vegetable at seedling stage based on deep learning and image processing. Journal of Jilin University (Engineering and Technology Edition), 2023; 53(8): 2421–2419. (in Chinese)
Ni C, Li Z Y, Zhang X, Zhao L, Zhu T T, Jiang X S. Film sorting algorithm in seed cotton based on near-infrared hyperspectral image and deep learning. Transactions of the CSAM, 2019; 50(12): 170–179. (in Chinese)
Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014; pp.580–587. doi: 10.1109/CVPR.2014.81.
Ren S Q, He K, Girshick R, Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015; 39(6): 1137–1149.
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y, Berg A C. SSD: Single shot multibox detector. In: Proceedings of the European Conference on Computer Vision – ECCV 2016, 2016; pp.21–37. doi: 10.1007/978-3-319-46448-0_2.
Redmon J, Divvala S, Girshick R, Farhadi A. You Only Look Once: Unified, Real-Time Object Detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas: IEEE, 2016; pp.779–788. doi: 10.1109/CVPR.2016.91.
Xiong J T, Liu Z, Tang L Y, Lin R, Bu R B, Peng H X. Visual detection technology of green citrus under natural environment. Transactions of the CSAM, 2018; 49(4): 45–52. (in Chinese)
Song Z Z, Fu L S, Wu J Z, Liu Z H, Li R, Cui Y J. Kiwifruit detection in field images using Faster R-CNN with VGG16. IFAC-PapersOnLine, 2019; 52(30): 76–81.
Li S J, Hu D Y, Gao S M, Lin J H, An X S, Zhu M. Real-time classification and detection of citrus based on improved single short multibox detecter. Transactions of the CSAE, 2019; 35(24): 307–313. (in Chinese)
Liu G X, Nouaze J C, Touko Mbouembe P L, Kim J H. YOLO-Tomato: A robust algorithm for tomato detection based on YOLOv3. Sensors, 2020; 20(7): 2145.
Wang J P, Gao K, Jiang H Z, Zhou H P. Method for detecting dragon fruit based on improved lightweight convolutional neural network. Transactions of CSAE, 2020; 36(20): 218–225. (in Chinese)
Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: Optimal speed and accuracy of object detection. arXiv 2020. arXiv: 2004.10934.
Han K, Wang Y H, Tian Q, Guo J Y, Xu C J, Xu C. GhostNet: More features from cheap operations. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020; pp.1577–1586. doi: 10.1109/CVPR42600.2020.00165.
Howard A, Sandler M, Chen B, Wang W J, Chen L-C, Tan M X, et al. Searching for MobileNetV3. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul: IEEE, 2019; 1314–1324. doi: 10.1109/ICCV.2019.00140.
Howard A G, Zhu M L, Chen B, Kalenichenko D, Wang W J, Weyand T, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv 2017, arXiv: 1704.04861.
Zhang X Y, Zhou X Y, Lin M X, Sun J. ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City: IEEE, 2018; pp.6848–6856. doi: 10.1109/CVPR.2018.00716.
Wu B C, Dai X L, Zhang P Z, Wang Y H, Sun F, Wu Y M, et al. FBNet: Hardware-aware efficient ConvNet design via differentiable neural architecture search. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019; pp.10726–10734. doi: 10.1109/CVPR.2019.01099.
Tian Y N, Yang G D, Wang Z, Wang H, Li E, Liang Z Z. Apple detection during different growth stages in orchards using the improved YOLO-V3 model. Computer and Electronics in Agriculture, 2019; 157: 417–426.
Wang D D, He D J. Recognition of apple targets before fruits thinning by robot based on R-FCN deep convolution neural network. Transactions of the CSAE, 2019; 35(3): 156–163. (in Chinese)
Mu L T, Gao Z B, Cui Y J, Li K, Liu H Z, Fu L S. Kiwifruit detection of far-view and occluded fruit based on improved AlexNet. Transactions of the CSAM, 2019; 50(10): 24–34. (in Chinese)
Copyright (c) 2024 International Journal of Agricultural and Biological Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.