Self-driving Vessels: YOLOv5 Approach for Water Surface Object Detection
Abstract
The use of Computer Vision techniques in Water Surface Object Detection has risen as a strong trend in autonomous vessels context. We propose an YOLOv5 algorithm performance avaliation for water surfaces objects. Following, we compare it with the benchmark results of others 17 classical detectors. This work uses an annotated image dataset available from a benchmark dataset, named WSSOD, characterized by being public, wide (7,467 images, 14 categories and differents capture conditions) and specialized in water surface objects. YOLOv5 reached a mAP of 76.3 %, outstanding in 11.3 % the mAP obtained by CRB-Net detector on the WSODD benchmark dataset.
Keywords:
self-driving vessels, object detection, water surface object detection
References
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Bloisi, D. D., Iocchi, L., Fiorini, M., and Graziano, G. (2011). Automatic maritime surveillance with visual target detection.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255.
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A. (2015). The pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, 111(1):98–136.
Girshick, R. B. (2015). Fast R-CNN. CoRR, abs/1504.08083.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press. http://www.deeplearningbook.org.
Gu, Y., Góez, J., Guajardo, M., and Wallace, S. (2019). Autonomous vessels: State of the art and potential opportunities in logistics. SSRN Electronic Journal, (2019/6).
He, K., Gkioxari, G., Dollár, P., and Girshick, R. B. (2017). Mask R-CNN. CoRR, abs/1703.06870.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (2001). Gradient-based learning applied to document recognition. In Intelligent Signal Processing, pages 306–351. IEEE Press.
Li, X., Tian, M., Kong, S., Wu, L., and Yu, J. (2020). A modified yolov3 detection method for vision-based water surface garbage capture robot. International Journal of Advanced Robotic Systems, 17(3):1729881420932715.
Lin, T., Goyal, P., Girshick, R. B., He, K., and Dollár, P. (2017). Focal loss for dense object detection. CoRR, abs/1708.02002.
Lin, T., Maire, M., Belongie, S. J., Bourdev, L. D., Girshick, R. B., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. (2014). Microsoft COCO: common objects in context. CoRR, abs/1405.0312.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S. E., Fu, C., and Berg, A. C. (2015). SSD: single shot multibox detector. CoRR, abs/1512.02325.
Prasad, D. K., Dong, H., Rajan, D., and Quek, C. (2020). Are object detection assessment criteria ready for maritime computer vision? IEEE Transactions on Intelligent Transportation Systems, 21(12):5295–5304.
Prasad, D. K., Rajan, D., Rachmawati, L., Rajabally, E., and Quek, C. (2017). Video processing from electro-optical sensors for object detection and tracking in a maritime environment: A survey. IEEE Transactions on Intelligent Transportation Systems, 18(8):1993–2016.
Ren, S., He, K., Girshick, R. B., and Sun, J. (2015). Faster R-CNN: towards real-time object detection with region proposal networks. CoRR, abs/1506.01497.
Ribeiro, R., Cruz, G., Matos, J., and Bernardino, A. (2019). A data set for airborne maritime surveillance environments. IEEE Transactions on Circuits and Systems for Video Technology, 29(9):2720–2732.
Wang, W., Shan, T., Leoni, P., Fernández-Gutiérrez, D., Meyers, D., Ratti, C., and Rus, D. (2020). Roboat II: A novel autonomous surface vessel for urban environments. CoRR, abs/2007.10220.
Xu, Q., Yang, Y., Zhang, C., and Zhang, l. (2017). Deep convolutional neural networkbased autonomous marine vehicle maneuver. International Journal of Fuzzy Systems, 20.
Zhao, Z., Zheng, P., Xu, S., and Wu, X. (2018). Object detection with deep learning: A review. CoRR, abs/1807.05511.
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., and Torralba, A. (2018). Places: A 10 million image database for scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6):1452–1464.
Zhou, Z., Sun, J., Yu, J., Liu, K., Duan, J., Chen, L., and Chen, C. L. P. (2021). An image-based benchmark dataset and a novel object detector for water surface object detection. Frontiers in Neurorobotics, 15.
Published
2022-07-31
How to Cite
SÁ, T. R. D.; FIGUEIREDO, C. M. S..
Self-driving Vessels: YOLOv5 Approach for Water Surface Object Detection. In: PROCEEDINGS OF BRAZILIAN SYMPOSIUM ON UBIQUITOUS AND PERVASIVE COMPUTING (SBCUP), 14. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 31-40.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2022.222855.
