黑烟车辆逐渐成为城市的主要污染源之一，针对黑烟的视频车辆检测方法具有效果好、成本低、应用面广和不妨碍交通等优点，但是仍存在误检率高、新方法可解释性差的缺陷。为了总结归纳视频黑烟检测算法的研究进展，本文对2016—2019年公开发表的文献进行总结。视频黑烟检测框架按顺序可以分为监控视频预处理、疑似黑烟区域选取、黑烟特征选取、分类识别和算法性能分析几部分，而且此顺序可以根据实际情况微调。本文介绍了视频黑烟检测框架，从层次的角度分析了疑似黑烟区域提取和黑烟特征选取。疑似黑烟区域提取方法由低到高依次分为图像级提取、目标级提取、像素级提取和纯黑烟重构等4个层次，提取方法的精细度与稳定性逐步上升，而且高层次方法一般可以应用在低层次方法的结果上。黑烟特征按基于学习的非线性映射次数划分为底层特征、中层特征和高层特征等3个层次，分界点是1次和3次。随着层次的提高，特征表达力就会越强，但二者之间并不是严格的线性关系。然后从可解释性的角度重点介绍了高层特征。另外，本文从有、无深度学习的角度归纳了特征提取算法，之后从传统方法与深度方法两方面归纳了常见的分类识别方法。最后介绍了主流算法评价指标。针对视频黑烟检测算法的几个特点，对其未来发展方向进行了总结归纳。

Smoke-emitting vehicles have gradually become one of the major pollution sources in cities. Algorithms that detect smoke-emitting vehicles from videos have good effects, low cost, and extensive applications. They also do not obstruct traffic. However, they still suffer from high false detection rates and poor interpretability. To fully reflect the research progress of these algorithms, this paper provides a comprehensive summary of articles published from 2016 to 2019. A video black smoke detection framework can be divided into surveillance video preprocessing, suspected smoke area extraction, smoke feature selection, classification, and analysis of algorithm performance. This order can be fine-tuned in accordance with the actual situation. This paper introduces and summarizes video smoke detection frameworks and analyzes the extraction of suspected smoky areas and the selection of smoke features from a hierarchical perspective. The method for extracting suspected smoky areas can be divided into four levels (from low to high): image-level extraction, object-level extraction, pixel-level extraction, and pure smoke reconstruction. The accuracy and stability of an extraction method gradually increases, and high-level methods can generally be applied to the results of low-level methods. Smoke features can be divided into three levels: bottom-, middle-, and high-level features. They are divided in accordance with the number of times of learning-based nonlinear projection, and demarcation points are once and three times. The expression of features becomes stronger and the false detection rate of black smoke decreases as level increases; however, the first two are not strictly linear. Then, the high-level features are generalized from the perspective of interpretability. In addition, this paper summarizes feature extraction methods from the perspective of the presence or absence of deep learning, and then classifies methods into traditional and deep learning. Lastly, the evaluation indexes of algorithms are introduced. At present, video smoke detection algorithms face three challenges: extracting features with increased expressiveness, improving generalization and interpretability, and estimating black smoke concentration. Considering these challenges, this paper provides suggestions on the future development direction of video smoke detection algorithms. First, the level of features should be rationally increased while ensuring expression and computational efficiency to improve feature fusion methods. Second, a deep neural network structure that considers generalization and interpretability should be designed in accordance with the space and motion characteristics of smoke. Further research should be conducted on how to fully alleviate the problems of insufficient number of smoky image training samples and uneven distribution. Third, an adaptive calibration algorithm should be designed to compare the extracted smoky gray level with the Lingerman standard black level.