Performance investigation of two-stage detection techniques using traffic light detection dataset

Abstract

Using a camera to monitor an object or a group of objects over time is the process of object detection. It can be used for a variety of things, including security and surveillance, video communication, traffic light detection (TLD), object detection from compressed video in public places. In recent times, object tracking has become a popular topic in computer science particularly, the data science community, thanks to the usage of deep learning (DL) in artificial intelligence (AI). DL which convolutional neural network (CNN) as one of its techniques usually used two-stage detection methods in TLD. Despite all successes recorded in TLD through the use of two-stage detection methods, there is no study that has analyzed these methods in experimental research, studying the strength and witnesses by the researchers. Based on the needs this study analyses the applications of DL techniques in TLD. We implemented object detection for TLD using 5 two-stage detection methods with the traffic light dataset using a Jupyter notebook and the sklearn libraries. We present the achievements of two-stage detection methods in TLD, going by standard performance metrics used, FASTER-CNN was the best in detection accuracy, F1-score, precision and recall with 0.89, 0.93, 0.83 and 0.90 respectively.