Automating Machinery with Object Detection using YOLO and Servo Controllers
Abstract
Now-a-days Computer Vision and Machine Learning algorithms play an important role in automation. With the help of Computer Vision and deep learning algorithms, data like images and videos are being used for classification and prediction. This paper proposes a real time object detector using computer vision and deep learning algorithms. YOLO (You Only Look Once) which is a deep learning algorithm is a state-of-the-art algorithm used for object detection. A binary classifier using CNN (Convolutional Neural Network) can be used to detect whether a class is present or absent indicating the presence or absence of a particular object. The two classes will be A) desired object present and B) the desired object absent. The input to the model will be from a live camera. The classifier detects the object by creating a bounding box around the object and then predicting the class. If class A is predicted the model will calculate the distance from the object. After calculating the distance, the model will instruct the machine to pick up the object and place it on the required position. If class ‘B’ is present the model will just display the message stating the class is absent.
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References
[2] Li, Chao Cao, Chu-qing Gao, Yun-feng. (2017). Visual Servoing based object pick and place manipulation system: Selected Papers from CSMA2016. 10.1515/9783110584998-036.
[3] Chen, W.; Xu, T.; Liu, J.; Wang, M.; Zhao, D. Picking Robot Visual Servo Control Based on Modified Fuzzy Neural Network Sliding Mode Algorithms. Electronics 2019, 8, 605. https://doi.org/10.3390/electronics8060605
[4] H. Wang, Y. Yu, Y. Cai, X. Chen, L. Chen and Q. Liu, "A Comparative Study of State-of-the-Art Deep Learning Algorithms for Vehicle Detection," in IEEE Intelligent Transportation Systems Magazine, vol. 11, no. 2, pp. 82-95, Summer 2019, doi: 10.1109/MITS.2019.2903518.
[5] Jiang, Z., Zhao, L., Li, S., & Jia, Y. (2020). Real-time object detection method based on improved YOLOv4-tiny. ArXiv, abs/2011.04244.
[6] Y. Lu, L. Zhang and W. Xie, "YOLO-compact: An Efficient YOLO Network for Single Category Real-time Object Detection," 2020 Chinese Control And Decision Conference (CCDC), 2020, pp. 1931-1936, doi:10.1109/CCDC49329.2020.9164580.
[7] T. -H. Wu, T. -W. Wang and Y. -Q. Liu, "Real-Time Vehicle and Distance Detection Based on Improved Yolo v5 Network," 2021 3rd World Symposium on Artificial Intelligence (WSAI), 2021, pp. 24-28, doi: 10.1109/WSAI51899.2021.9486316.
[8] Xu, J., Li, Z., Du, B., Zhang, M., Liu, J. (2020). Reluplex made more practical: Leaky ReLU. 2020 IEEE Symposium on Computers and Communications(ISCC)doi:10.1109/iscc50000.2020.9219587 10.1109/ISCC50000.2020.9219587
[9] Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., . . . Summers, R.M. (2016). Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Transactions on Medical Imaging, 35(5), 1285–1298. doi:10.1109/tmi.2016.2528162
[10] Jan Hosang, Rodrigo Benenson, Bernt Schiele; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 45074515
[11] J. Yang, X. Fu, Y. Hu, Y. Huang, X. Ding and J. Paisley, "PanNet: A Deep Network Architecture for Pan-Sharpening," 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1753-1761, doi: 10.1109/ICCV.2017.193.
[12] Bochkovskiy, Alexey & Wang, Chien-Yao & Liao, Hong-yuan. (2020). “YOLOv4: Optimal Speed and Accuracy ofObjectDetection”.doi:https://doi.org/10.48550/arXiv.2004.1093
[13] Redmon, Joseph and Farhadi, Ali, “YOLOv3: An Incremental Improvement”, arXiv, 2018, 10.48550/ARXIV.1804.02767
[14] Lin, Tsung-Yi and Dollár, Piotr and Girshick, Ross and He, Kaiming and Hariharan, Bharath and Belongie, Serge, “ Feature Pyramid Networks for Object Detection”, arXiv, 2016, doi : 10.48550/ARXIV.1612.03144
[15] Nepal, Upesh & Eslamiat, Hossein. (2022). Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs. Sensors. 22. 10.3390/s22020464.
[16] Liu, Shu and Qi, Lu and Qin, Haifang and Shi, Jianping and Jia, Jiaya, “Path Aggregation Network for Instance Segmentation”,arXiv, 2018, 10.48550/ARXIV.1803.01534
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