# DB-Net: Detecting Vehicle Smoke with Deep Block Networks
## 1 Introduction  
Over the last few decades, the number of vehicles has been increasing worldwide, especially in developing countries [1]. With the increase in the number of vehicles, vehicle emissions are becoming one of the significant sources of air pollution in many areas, including carbon dioxide, volatile organic compounds, and particulate matter, which have been associated with many adverse human health effects [2]. Visible vehicle emissions, a heavily polluted type of vehicle emission, are defined as vehicle smoke. A typical example of vehicle smoke is shown in Figure 1. The popular methods for detecting vehicle smoke rely on the manual report from traffic police or analysis of the vehicle emissions by an exhaust gas analyzer, which is less efficient and extremely expensive. Due to these limitations, detecting vehicle smoke in surveillance videos has recently drawn widespread attention, which provides a broader monitoring field and faster response than the popular methods. Detecting vehicle smoke in surveillance videos aims to locate the region of vehicle smoke in the video frame, the core of which is detecting smoke. Vision-based smoke detection has been studied extensively in the past 20 years [3]. However, most existing research has focused on the wild scenario [4; 5; 6], with little attention paid to road vehicle scenarios (i.e., vehicle smoke detection). Compared with the wild scenario, it is more cluttered in the road vehicle scenario, such as shadows from the rear of vehicles and interference between vehicles, which makes vehicle smoke detection a more challenging problem. Although many methods for detecting smoke in the wild scenario can be transplanted to vehicle smoke detection, these transplanted methods still struggle in detection performance due to the significant difference between wild and road vehicle scenarios. Existing vehicle smoke detection methods can be roughly categorized into traditional image process-based and deep learning-based methods. Traditional image process-based approaches usually rely on hand-crafted features to solve the smoke detection problem, which recognizes smoky and non-smoky regions according to different characteristics. There are two critical steps in the traditional image process-based methods. Firstly, extracting hand-crafted features according to the visual or statistical clues from regions, such as texture features [7], dynamic features [8], and spatiotemporal features [9]. After feature extraction, a classifier such as the K-Nearest Neighbors (K-NN) classifier [10] or Support Vector Machine (SVM) [11] is used to categorize these regions as non-smoky and smoky regions. Research on the traditional image process-based approach mainly focuses on designing interpretable and efficient feature descriptors to improve the recognition accuracy of the smoke region. However, the performance of these feature descriptors is dependent on prior knowledge and artificial design, which the complex environment can severely influence. For example, the dynamic features of the smoke can be changed by vehicle speed, and different backgrounds could make the texture feature of smoke significantly different. Recently, deep learning techniques have been applied to various computer vision tasks and made tremendous progress, including object detection and semantic segmentation [12; 13]. Both deep learning-based object detection and semantic segmentation approach can be applied in vehicle smoke detection. Object detection approaches for vehicle smoke detection provide rough location information, while semantic segmentation approaches realize pixel-level prediction by outputting a mask that contains classification, localization, and detailed depiction of boundaries. Nevertheless, semantic segmentation for vehicle smoke detection still has some challenges. The first problem is that training the semantic segmentation model requires extensive and high-quality labeled data. Even worse, there is still no public semantic segmentation dataset for vehicle smoke in the community.