Vehicle Safe Distance Detection System Based On Image Processing As Accident Prevention With Faster R-CNN Method - Agus Khumaidi Khumaidi; Elok A Chandra; Perwi Darmajanti; Ivan A. Septiadi; Sryang T. Sarena (Universitas Nahdlatul Ulama Surabaya, 2022)
Numerous victims and huge economic and social losses have resulted from the escalating number of traffic accidents. From these issues, a technique to create a camera capable of detecting vehicles going around the driver using the Faster R-CNN method and calculating the vehicle's distance using the Stereo Vision and Mono Vision methods was discovered. The determination of safe distance between these cars is determined by the speed of the driver's vehicle, with the LED and buzzer warning system activating when the parameters are met. Based on the results of object detection experiments utilizing the Faster R-CNN, the model's success rate in identifying and classifying objects had an average success rate of 83.33 percent across 35 object situations examined from different perspectives. The success rates for distance estimates utilizing the Stereo Vision and Mono Vision methods with the Linear Regression equation were 98.84% and 98.10%, respectively.