Abstract
In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. First, Retinex image enhancement algorithm was introduced to improve the quality of images, collected under low-visibility conditions (e.g., heavy rainy days, foggy days and dark night with poor lights). Then, a Yolo v3 model was trained to detect multiple objects from images, including fallen pedestrians/cyclists, vehicle rollover, moving/stopped vehicles, moving/stopped cyclists/pedestrians, and so on. Then, a set of features were developed from the Yolo outputs, based on which a decision model was trained for crash detection. An experiment was conducted to validate the model framework. The results showed that the proposed framework achieved a high detection rate of 92.5%, with relatively low false alarm rate of 7.5%. There are some useful findings: (1) the proposed model outperformed empirical rule-based detection models; (2) image enhancement method can largely improve crash detection performance under low-visibility conditions; (3) the accuracy of object detection (e.g., bounding box prediction) can impact crash detection performance, especially for minor motor-vehicle crashes. Overall, the proposed framework can be considered as a promising tool for quick crash detection in mixed traffic flow environment under various visibility conditions. Some limitations are also discussed in the paper.
Highlights
Emergency response to roadway crashes is very important for tra c management
People injured in a crash need to be sent to the nearest hospital in the rst place to prevent their health condition from being worsened, on the other hand, serious crashes o en cause nonrecurrent congestions, if emergency response or clearance is not carried out in time
Such method is o en inaccurate due to systematic errors caused by both algorithms and data quality [1,2,3,4,5]. us, in practice, crashes were o en detected by human observers through CCTV in Tra c Management Centers (TMC). e advantage of CCTV is that it can directly capture crash scenes within its range
Summary
Emergency response to roadway crashes is very important for tra c management. On the one hand, people injured in a crash need to be sent to the nearest hospital in the rst place to prevent their health condition from being worsened, on the other hand, serious crashes o en cause nonrecurrent congestions, if emergency response or clearance is not carried out in time. Crash detection can be conducted by analyzing tra c ow data from roadway detectors, such as loops and microwaves Such method is o en inaccurate due to systematic errors caused by both algorithms and data quality [1,2,3,4,5]. Us, it is very meaningful to develop other reliable automatic crash detection methods based on CCTV [6, 7]. Us, researchers have been focusing on developing crash detection models based on complex deep learning frameworks [10, 11]. Considering these, a vision-based crash detection model framework was developed for mixed tra c ow environment in this study. E paper is organized as follows: the second section discuss previous literature related to vision-based crash detection and image enhancement.
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