Vehicle Exhaust Estimation Using YOLOv7 and Support Vector Regression with Image Features

Vehicle exhaust is a major source of air pollution that contributes to environmental degradation and poses risks to public health. This paper presents an image-based method to estimate opacity (OP) and particulate matter (PM) from vehicle exhaust. In the proposed method, YOLOv7 was used to identify vehicles and, thus, the region of interest (ROI). Then, a support vector regression was trained, with four image features extracted from the ROI as the input vectors, while OP or PM was used as the output. The proposed method was verified by experiments where moving and static scenarios with three ROIs were considered. The ROIs used in the experiments were exhaust pipe area (EPA), vehicle bounding box (VBB), and white background (WBG). In the moving scenario, the EPA and VBB ROIs were considered. For the VBB ROI, the average R2 values for OP and PM in the given examples were 0.834 and 0.894, respectively. For the EPA ROI, the average R2 values for OP and PM were 0.838 and 0.910, respectively. In the static scenario, the EPA and WBG ROIs were considered. For the EPA ROI, the average R2 values for OP and PM were 0.619 and 0.612, respectively. For the WBG ROI, the average R2 values for OP and PM were 0.748 and 0.732, respectively. The results suggest that the EPA ROI is preferable in the moving scenario and the WBG ROI in the static scenario to estimate OP and PM from vehicle exhaust. The proposed method is promising in the estimation of OP and PM from vehicle exhaust because satisfactory R2 values were achieved.