Smoke detection on roads for autonomous vehicles

This paper describes the smoke detection algorithm for autonomous vehicles equipped with camera and lidar. The main feature is the ability to detect smoke with ego motion of the camera. Color characteristics of smoke are used to detect regions of interest by similarity of pixels between the current frame and the training data. The following metrics are used: red, green, blue, cyan, saturation channels and spatial entropy. Each region of interest is then enhanced by removing small objects and by filling holes. Sky region is removed by checking edge density of the region. Other rigid objects are expelled by the boundary roughness feature. By knowing the fact that smoke tends to change its shape in frame sequence, the angle-radius shape descriptor is introduced. Cross-correlation of this descriptor between regions in consequent frames will show objects with not appropriate behavior. Data from the camera and lidar are fused to make the final decision.