An object detection method for the work of an unmanned sweeper in a noisy environment on an improved YOLO algorithm

作者：Huo, Junliang; Shi, Baijun^*; Zhang, Yihuai

来源：Signal Image and Video Processing, 2023, 17(8): 4219-4227.

DOI：10.1007/s11760-023-02654-4

• Unmanned sweepers
• YOLO-v5
• Vibration response
• Loss function
• Attention mechanism

Efficient and accurate object detection is crucial for the widespread use of low-cost unmanned sweepers. This paper focuses on the low-cost sweeper in practical working scenarios and proposes a traffic participant detection method based on an enhanced YOLO-v5 model. To train the model on noise knowledge, three types of noise are added to the data set in the offline phase, according to the vibration response of the mathematical model, and the impact of the low-cost camera. The loss function was optimized to balance detection accuracy and real-time performance while focusing on traffic participant detection using YOLO-v5. CTDS and BFSA modules were proposed based on the attention mechanism to enhance the YOLO-v5 model. Comparative experiments demonstrated the effectiveness of the proposed method, with the enhanced YOLO-v5 model achieving a 4.5% higher mean average precision than the traditional YOLO-v5 network. Moreover, the proposed method can process images at a frame per second of 89 while ensuring real-time performance, meeting the object detection requirements of actual sweeper.