Improved Vehicle Detection Accuracy Using CLAHE Andi Widiyanto, Setiya Nugroho, Muhammad Resa Arif Yudianto
Universitas Muhammadiyah Magelang
Abstract
The large number of vehicles can cause new problems in various fields .Vehicle detection errors can occur in the vehicle detection system when several vehicles are side by side so that they are not detected or are detected as larger vehicles. This research produces a vehicle type detection system to improve Vehicle Detection Accuracy by applying image processing on Convolutional Neural Network (CNN). In this study, experiments were conducted with 20 image processing scenarios in the pre-processing image before the training process to produce an object detection testing model. The simulation test results show that not all image processing scenarios can improve the accuracy of the detection process. The combined image processing scenario of Blue Channel + CLAHE + gaussian filter + thresholding produces an accuracy of 97%.
