Visual-Based Motorcycle Detection using You Only Look Once (YOLO) Deep Network

Abstract

In Malaysia and various Asian countries, use of motorcycle is growing in numbers, and the motorcycle is the dominating transport mode. In fact, the number of motorcycles per thousand people averaged over several major Asian cities is significantly higher than the average of the rest of the world. With growing use of motorcycle, blind spots have become one of major cause to road injuries and fatalities of motorcyclists in Malaysia. In this work, a visual-based detector is proposed to reduce the risk of blind spots in causing road injuries to motorcyclists. The objective of this detector is to provide alerts to the drivers of cars and other vehicles when there are motorcyclists in proximity to the vehicles especially around blind spot areas. In developing this solution, a variant of deep network called You Only Look Once (YOLO) is chosen as visual-based detector. This YOLO deep network is trained and tested using the 5811 collected images of motorcyclists. A benchmark with regards to its accuracy and speed is conducted by comparing this visual detector against several methods such as Aggregate Channel Features (ACF) and Faster Region Convolutional Neural Network (FRCNN). Results showed that YOLO detector is the most superior detector since it has the best average precision out of all detectors, and its inference time at 22.55 ms (44 fps) is able to provide real-time implementation. Besides, YOLO inference on machines without GPU still manage to achieve a commanding performance, which is on average, at 17 fps.