# Cascaded Vehicle Matching and Short-Term Spatial-Temporal Network for Smoky Vehicle Detection  
[PERSON]  
1 College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China; [EMAIL_ADDRESS] (X.F)  
[PERSON]  
1 College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China; [EMAIL_ADDRESS] (X.F)  
[PERSON]  
1 College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China; [EMAIL_ADDRESS] (X.F)  
[PERSON]  
2 Bus  
dominated smoke recognition. For example, wavelet features were used in [14] and local binary patterns were used in [17; 18; 20; 22; 24]. Considering the motion information of smoke, motion orientation features [16], dynamic texture features [18], and 3D local difference features [19] are also widely used. After feature extraction for regions, a classifier such as support vector machine (SVM) [25] is used to categorize each region as smoke and non-smoke regions. Recently, with the development of deep learning [26], convolutional neural networks have been extensively used for smoke recognition [27; 28; 29; 30; 31; 32; 33]. Reference [27] evaluated different convolutional neural networks for image-based smoke classification including AlexNet [26], ResNet [34], VGGNet [35], etc. Three-dimensional (3D) convolutional neural networks were applied for smoke sequence recognition in [28; 29; 32]. Recurrent networks were also explored for sequence-based smoke recognition in [31]. Almost all the mentioned methods focus on general wildfire smoke or forest wildfire smoke as shown in the left and middle of Figure 1.  
Intuitively, the above smoke recognition methods can be used for smoke vehicle detection without considering vehicles. Along this direction, [PERSON] et al. [7] leveraged a codebook model for background modeling and applied volume local binary count patterns, which achieved impressive smoke detection results in strong smoky traffic videos. However, there exist essential differences between wildfire smoke and vehicle smoke as shown in Figure 1. First, the smoke emitted from vehicles is mostly slighter than wildfire smoke, and occupies only a small region in video frames. Second, due to smoke being emitted from the bottom of vehicles, algorithms are apt to confuse smoke, shadows, and rainy road surfaces. Though much progress has been made in recent years [4; 5; 6; 7; 8; 9; 10; 11], the false alarm rate over frames is still high (e.g., around 13% in [3]), which makes it hard to meet practical applications. Worse still, unlike wildfire smoke recognition, there is no publicly available vehicle smoke data in the community. The above issues make smoky vehicle detection more challenging and it is still an open problem. In this paper, to address these challenges, we first introduce a real-world Large-Scale Smoky Vehicle image dataset, termed LaSSoV, which is the largest vehicle smoke dataset to date. In total, the LaSSoV contains 75,000 smoky vehicle images with bounding box annotations, which is expected to facilitate the effective training of deep learning models in smoky vehicle detection. To simulate practical scenarios, we also provide a smoky vehicle video dataset, termed LaSSoV-video, which includes 163 segment-annotated long videos in diverse environments. Second, we propose a novel efficient cascaded framework which fully makes use of prior knowledge and deep neural networks. In the first step of this framework, we improved the famous YOLOv5 object detector with minimum width and depth in its architecture, and also replaced most of the convolutional operations with depth-wise and point-wise convolutional operations inspired by MobileNet [36] and GhostNet [37]. In the second step, with the prior knowledge that smoke must be emitted from vehicles, we designed a vehicle matching module which computes the Intersection-over-Union (IoU) ratios or pair-wise distances between smoke proposals and vehicle regions. The smoke  
Figure 1: Smoke detection in different environments. (**Left**): general wildfire smoke [18].