A Modified Model with Multi-Scale Feature Fusion and Multi-Decoupled-Head for Detecting Traffic Object

Accurately and rapidly detecting traffic object has been attracted intensive attention due to its potential applications in the fields of autonomous driving, traffic flow monitoring, augmented reality (AR) and so on. However, there are many difficulties in the process of traffic object detection indeed, such as occlusion and aggregation between objects, insufficient feature extraction of objects, in particular the presence of a large number of small objects, which bring great challenges to these traffic objects detection. In this paper, an improved traffic object detection model based on You-Only-Look-Once version 5 small (YOLOv5s) is proposed to address the issues. By utilizing spatial pyramids to extract multi-scale spatial features and applying Squeeze-and-Excitation (SE) channel attention to capture more global and local semantic features, especially by designing a sub-network in the neck to fuse high-resolution information in shallow layers with more accurately semantic information in deep layers, the detection sensitivity of object features is enhanced. More importantly, by explanting decoupled-head into the network, outstanding performance of the model with high detection accuracy and rapid detection speed is realized. The experimental results on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) and Laboratory for Intelligent and Safe Automobiles (LISA) traffic signs datasets both show that the modified model significantly improves the detection accuracy. Meanwhile, the high real-time performance is still maintained. Undoubtedly, the modified model proposed in this paper can effectively address many difficulties in traffic object detection under various complex scenes, which would be greatly helpful for its potential applications in the future.