Abstract
The obtainment of road condition information during driving is extremely important for a driver. However, drivers usually cannot notice multiple information at the same time, which definitely increases certain safety risks. Considering this problem, this paper designs a road information collection plus alarm system based on artificial intelligence to monitor road information. The underlying core algorithm of this system adopts the YOLO v3 network with the best comprehensive detection performance in the end-to-end network. We use this network’s advantage of fast detection speed to optimize on its original basis, and propose to “copy” part of the backbone network to build an auxiliary network, which enhances its feature extraction capability. Further, we apply the attention mechanism to the feature information fusion of the auxiliary network and the backbone network, suppress the invalid information channel, and improve the network processing efficiency. Besides, the training part of the network is optimized, and the mAP (mean Average Precision) is improved by setting the scale that meets the target to be detected. Through the test, the average test accuracy of the optimized network model reaches 84.76%, and the real-time detection speed on the 2080Ti reaches 41FPS. Compared with the previous network, the detection accuracy increases by 5.43% after optimization.
Highlights
In recent years, road condition information recognition technology plays an important role in advanced driving assistance and automatic driving of unmanned vehicles as an important part of intelligent driving system
Literature [6] adopts the geodesic transformation (GDT) method to generate the distance map based on superpixel, and the classification performance is significantly improved by incorporating the shape feature
This paper mainly introduces the detection of road information based on our-yolo network model optimized by Yolo V3 network
Summary
Road condition information recognition technology plays an important role in advanced driving assistance and automatic driving of unmanned vehicles as an important part of intelligent driving system. YOLO v3 uses a multi-scale detection mechanism to detect the feature maps of 13×13, 26×26 and 52×52, respectively, and enhances the ability to extract small targets. Its network structure is shown in Figure 3: YOLO v3 uses 3 different scale feature maps to predict the detection results.
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