Learning Discriminative Feature Representation for Estimating Smoke Density of Smoky Vehicle Rear

Estimating the smoke density from a single image of the smoky vehicle rear is significant for smoke level estimation and smoky vehicle recognition. However, this is a highly ill-posed problem. To solve it, this paper presents a novel smoke density estimation network (SDENet). First, to reduce the susceptibility of deeper convolutions towards the smoke scale variant and enhance feature diversity, we propose the attention multi-scale encoding blocks based on multi-scale blocks and the convolutional block attention. The multi-scale block exploits the multi-scale features at a granular level within a single basic block. The attention makes our model pay more attention to specific regions which are beneficial to smoke density estimation. Second, the extracted features for smoke recognition may contain background interference information due to the smoke translucency, so we propose semantic-guided feature selection blocks to progressively select smoke-relevant features and suppress background interference features from the encoded feature via global high-level semantic information for learning more discriminative features. Finally, to make the learned features more adaptive to smoke feature resolution and visual appearance, we design attention gate decoding blocks to fuse different features via gate blocks, which enhance the features at spatial locations and channel locations where the features are essential for smoke density estimation. Extensive experiments on smoke density estimation show that our model achieves the best performance among existing methods.