Integrated Traffic Incident Classification using SegFormer and Faster R-CNN: A Multi-Stage Approach for Enhanced Detection and Analysis

A traffic incident refers to any incidence or situation that interrupts the regular movement of car traffic or presents a hazard to individuals using the road. These incidents comprise a wide range of accidents involving vehicles, including rear-end crashes, head-on accidents, or side-impact crashes, which can lead to trauma, destruction, or property damage. This also includes vehicle failures caused by mechanical failures, such as engine problems or flat tires, which render cars immobile. The proposed paper has a state-of-the-art method for categorizing traffic incidents, which combines two sophisticated computer vision models: SegFormer and Faster R-CNN. SegFormer, an advanced semantic segmentation feature, creates detailed pixel-by-pixel classification maps of traffic scenes. This feature facilitates the clear distinction of various traffic actions and components. The final segmentation result offers a broad understanding of the spatial arrangement and corresponding information within the image. In addition, a Faster R-CNN for object detection has outstanding performance in detecting and categorizing distinct items such as automobiles and pedestrians. In the segmented regions, faster R-CNN improves the detection and classification of specific traffic-related items. The efficiency of the proposed approach is measured using a collection of traffic images, showing improved ability in identifying and classifying several forms of traffic hazards. The findings demonstrate that this hybrid method greatly surpasses conventional single-model techniques, providing a more robust and complete solution for the study and control of traffic incidents.