# Three-Dimensional Convolutional Vehicle Black Smoke Detection Model with Fused Temporal Features
## 2 Related Works
### Black Smoke Detection  
Before the popularity of deep learning-based black smoke detection, previous methods were mainly based on traditional image processing to accomplish the black smoke detection task, such as gradient features, texture features, and frequency domain features. For example, [PERSON] et al. [20] employed color histogram, local binary pattern, and orientation gradient histogram to characterize smoke in the HSV color space. They further extracted frequency domain information of vehicle black smoke using discrete cosine transform for the final classification.  
The advent and progression of deep learning have significantly advanced vehicle black smoke recognition, and become the mainstream solution for black smoke detection. Among these solutions, representative approaches are the following:  
* [PERSON] et al. [21] enhanced network structure with MobileNet and spatial pyramid pooling and adopted a transfer learning training strategy. The recognition of black smoke was achieved by analyzing multiple frames, yielding improved results, albeit with challenges such as a high false alarm rate, a high rate of missed detections, and significant computational expense. Starting from this work, the model architecture used for black smoke detection started to converge towards mainstream target detection models.
* [PERSON] et al. [3] proposed a two-stage convolutional neural network that combines the strengths of YOLOv3 and a multi-region convolutional network, effectively achieving fine-grained identification of black smoke. This is the first time that YOLO series model has been used in a black smoke detection mission, demonstrating the power of the YOLO line of models.
* [PERSON] et al. [22] utilized YOLOv3 for vehicle target detection and subsequently employed the Vision Transformer [23] to identify black smoke emanating from the rear of vehicles. This is a landmark attempt to apply the attention mechanism in a large number of applications to black smoke detection tasks.
* [PERSON] et al. [4] introduced a black smoke recognition method based on ResNet, which incorporates a reinforced spatial attention mechanism to effectively reduce the false alarm rate.  
In addition to these representative efforts mentioned above, many other works have contributed to the progress of black smoke detection. [PERSON] et al. [24] introduced a method that leverages spatial information for black smoke vehicle detection. This method employed the Inception V3 model [25] to capture spatial information from suspect frames. [PERSON] et al. [9] proposed a one-stage diesel vehicle black smoke detection network namely CFL-Net based on color features location for environmental inspection stations. [PERSON] et al. [8] proposed a detection method based on a two-stage segmentation-detection paradigm. It is an integrated approach of a semantic segmentation model and a detection model. This approach allows the segmentation model to filter out image regions unrelated to black smoke, resulting in more refined inputs for the detection model.  
However, the above approach still suffers from two weaknesses:  
* Impractical detection scenarios: Most of the work mentioned above focuses primarily on model design and does not discuss the practical black smoke detection scenarios. On the other hand, for example, although [9] consider the issue of the detection scenarios, their model is designed for a specialized annual inspection station, which increases the cost of detection.
* Less usage of the temporal information: As we mentioned, temporal information has unique advantages for the black smoke detection task, yet previous methods have not discussed these advantages in detail. We discuss these two disadvantages in the next two sections.