Obstacle detection based on improved YOLOV8

Zhifeng Zhang; Tong Zhang

doi:10.1117/12.3038456

23 August 2024 Obstacle detection based on improved YOLOV8

Zhifeng Zhang, Tong Zhang

Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 132502U (2024) https://doi.org/10.1117/12.3038456
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia

Abstract

To address the issues of inaccurate and incomplete obstacle detection, this paper proposes training an obstacle detection model using deep learning techniques. Given the diverse and complex nature of various obstacles, improving detection accuracy and multi-object capturing capability is crucial. An improved YOLOV8 model is proposed in this paper, utilizing object detection algorithms for obstacle detection. Firstly, a Convolutional Block Attention Module (CBAM) is integrated into the feature extraction network of the object detection backbone to enhance the network's focus on obstacle targets. Furthermore, the DeepSort algorithm is combined with the YOLOV8 object detection algorithm to enhance the capability of capturing multiple and dynamic targets. Experimental results demonstrate that the improved YOLOV8 model achieves an mAP (mean average precision) of 94.2% and an accuracy of 95.0%. Compared to the original YOLOV8 model, the accuracy has increased by 3.9%, and the mAP value has increased by 3.7%. Moreover, it outperforms several other object detection models. The research methodology presented in this paper can provide valuable insights for the field of obstacle detection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhifeng Zhang and Tong Zhang "Obstacle detection based on improved YOLOV8", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 132502U (23 August 2024); https://doi.org/10.1117/12.3038456

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