DB-Net: Detecting Vehicle Smoke with Deep Block Networks

Abstract

Vision-based vehicle smoke detection aims to locate the regions of vehicle smoke in video frames, which plays a vital role in intelligent surveillance. Existing methods mainly consider vehicle smoke detection as a problem of bounding-box-based detection or pixel-level semantic segmentation in the deep learning era, which struggle to address the trade-off of localization accuracy and speed. In addition, although various studies have been reported, there is no open benchmark available for real vehicle smoke detection. To address these issues, we made three contributions as follows: (i) We built a real-world vehicle smoke semantic segmentation dataset with 3962 polygon-based annotated vehicle smoke images, which will be released to the community. (ii) We regard vehicle smoke detection as a block-wise prediction problem and propose a conceptually new, yet simple deep block network model (DB-Net). It provides more accurate localization information than bounding-box-based ones and has a lower computational cost than semantic segmentation methods. (iii) We introduce a coarse-to-fine training strategy, where we first pre-train a model on bounding-box annotated data and then fine-tune it on pixel-wise labeled data. We compare our DB-Net to several advanced methods and evaluate them in several metrics. Extensive experiments demonstrate that our method is significantly superior to other methods.