Early Warning Sistem Rambu Pembatas Kecepatan menggunakan Histogram Oriented Gradient dan Klasifikasi SVM berbasis Raspberry Pi - Masyita Lionirahmada; Fitri Utaminingrum (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2021)
Traffic accidents happened frequently due to the lack of public attention to traffic sign regulations. Along with the many cases of accidents and the high number of deaths caused by public negligence in understanding the meaning of traffic signs correctly, an early warning is needed to understand the traffic signs listed on the road by making an early warning system for speed limiting signs. In this study, a system for detecting speed limiting signs was developed using feature extraction Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) classification to classify the types of speed limiting signs. To carry out the detection process for speed limiting signs, this system uses a Pi camera to take the video of the speed limit signs to be detected. When the speed limiting signs is detected, the speaker will release a warning sound to make it easier for drivers to drive and comply with traffic regulations. The system testing process is carried out by looking at the results of the implementation system that can detect the signs and the level of system accuracy when it detects speed limiting signs. The average accuracy of the system from the detection results consisting of signs limiting the maximum speed of 20km, 25km, 30km, 40km, 50km and the minimum of 20km is 86.08%. In addition, system testing is carried out by driving following the speed of the vehicle listed on the speed limit sign to determine whether the system can detect it properly by following the speed direction on the speed limiting sign.