# Three-Dimensional Convolutional Vehicle Black Smoke Detection Model with Fused Temporal Features
###### Abstract  
The growing concern over pollution from vehicle exhausts has underscored the need for effective detection of black smoke emissions from motor vehicles. We believe that the optimal approach for the detection of black smoke is to leverage existing roadway CCTV cameras. To facilitate this, we have collected and publicly released a black smoke detection dataset sourced from roadway CCTV cameras in China. After analyzing the existing detection methods on this dataset, we found that they have subpar performance. As a result, we decided to develop a novel detection model that focuses on temporal information. This model utilizes the continuous nature of CCTV video feeds rather than treating footage as isolated images. Specifically, our model incorporates a 3D convolution module to capture short-term dynamic and semantic features in consecutive black smoke video frames. Additionally, a cross-scale feature fusion module is employed to integrate features across different scales, and a self-attention mechanism is used to enhance the detection of black smoke while minimizing the impact of noise, such as occlusions and shadows. The validation of our dataset demonstrated that our model achieves a detection accuracy of 89.42%,showing around 3% improvement over existing methods. This offers a novel and effective solution for black smoke detection in real-world applications. vehicle black smoke detection; feature extraction; 3D convolution; feature fusion +
Footnote †: journal: Medical  
however, this model requires further refinement to improve its accuracy due to its lack of consideration for black smoke motion. [PERSON] et al. [5] reconstructed a multi-scale network model based on YOLOv5 [6], replacing the path aggregation network with a bidirectional weighted feature pyramid network, which enhanced feature fusion and adjusted feature contributions, resulting in reduced leakage and misdetection rates. However, the method above only extracts the black smoke feature information in a single image and adopts the methods of improving the model structure and introducing the attention mechanism to identify the black smoke. In actual video data, a single frame captures only a portion of the black smoke information present in the entire video sequence. Black smoke varies in morphology, concentration, and diffusion across different frames and can be easily confused with the shadows of vehicles and road stains, leading to significant interference in subsequent processing. Additionally, the black smoke in the road surveillance video occupies a small area in the image, making it challenging to determine its existence only by a single image, which leads to the low detection accuracy of the above methods. We advocate that the optimal approach for detecting black smoke is to leverage the existing roadway CCTV cameras. These cameras are typically integrated into the city's infrastructure and are currently extensively employed for monitoring speeding violations and red-light runners, among other functions. They typically offer a broad field of view and are designed to capture continuous video footage rather than just intermittent snapshots. By analyzing the same instance across different frames, it is possible to tackle many difficult cases, such as those with a blurry appearance and background occlusion [7]. Note that this approach is particularly advantageous for black smoke detection because black smoke itself is very blurry and the motion shadows produced by motorized vehicles are visually very similar to emitted black smoke [8; 9]. With the above perspectives, we realize that one of the reasons that the prevailing existing methodologies do not employ the aforementioned optimal detection paradigm is due to the constraints inherent in the available datasets. To the best of our knowledge, a dataset for black smoke detection that utilizes roadway CCTV cameras, incorporating temporal attributes, does not yet exist.