Obstacle detection based on improved YOLOV8

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.