DSOD: A Novel Method for Intelligent Traffic Object Detection

Abstract Accurate identification of road object is crucial for intelligent traffic systems. However, due to the complexity of road traffic scenarios, developing efficient and accurate road object detection methods has been a challenging task. In this study, a new improved method for road object detection is proposed, named Enhanced YOLOv5 algorithm and Deep Schedule Object Detection (DSOD) algorithm. Real traffic scenes from the BDD100k dataset are used for training and testing the object detection model. The dataset consists of 9 different types of road objects in various traffic scenarios. The Mosaic data augmentation algorithm is applied to merge images in the dataset. Mean Average Precision (mAP), Precision (P), and Recall (R) are used as evaluation metrics to compare the enhanced YOLOv5 model with the most common models. Experimental results demonstrate that the DSOD algorithm achieves success in intelligent traffic object detection, significantly improving the accuracy and robustness of road object recognition. Additionally, the developed model shows significant performance improvement in accurately identifying objects in complex traffic scenes. These results suggest that the DSOD algorithm is a promising choice for intelligent road recognition and can easily adapt to different traffic scenarios. Furthermore, employing cloud computing for real-time detection meets the requirements of intelligent cooperative vehicles and enhances their visual perception capabilities.