A Multi-branch Network with Internal Feature Fusion for Road Risk Detection

To detect and locate road objects precisely is crucial for the routing and navigation of autonomous vehicles and robots. A deep neural network can be used for this purpose but the inference cost is usually too high for real-time processing with low-cost devices. How to guide the network to attend risky areas plays an essential role in cost reduction. This paper proposes a novel multi-branch network that selectively extracts pre-specified features and detects road risks in corresponding regions of interest through different branch networks. The proposed network consists of one common block to obtain global features and several branches to detect road risks in particular interested regions. Experimental results show that our method can reduce the inference cost significantly while preserving accuracy.