MC-Net: Multi-Scale Feature Fusion and Cross-Level Information Interaction Network for Traffic Sign Detection
Zhongyi Yu, Debo Cheng, Wenzhen Zhang, Jing Chen, Shichao Zhang · 2023
Traffic sign detection is an important topic in autonomous driving and intelligent transportation, as it is widely applied in real-life scenarios to detect crucial road information for autonomous devices. However, existing detection methods face challenges due to significant variations in the scale of target objects and limited preservation of contextual information. To address these challenges, we propose MC-Net, a novel traffic sign detection network that enhances the model’s receptive field and enriches contextual information features. To overcome the sensitivity of detectors on the target scale variation, we first introduce the Multi-Scale Feature Fusion (MFF) module. By incorporating dilation convolution and group convolution, the MFF module effectively expands the network’s receptive field and reduces sensitivity to scale variations. Additionally, we incorporate a cross-layer information interaction (CLII) module into the MC-Net model, which facilitates the extraction of feature information across diverse network layers and effectively addresses the issue of contextual information loss. Experimental results on two real-world datasets (TT100K and STSD), demonstrate that MC-Net significantly improves detection efficiency compared to existing methods, achieving mAP of 85.7% and 94.8% respectively.