Deep Learning in Vehicle Detection Using ResUNet-a Architecture

Abstract

Vehicle detection is still a challenge in object detection. Although there are many related research achievements, there is still a room for improvement. In this context, this paper presents a method that utilizes the ResUNet-a architecture – that is characterized by its high accuracy - to extract features for improved vehicle detection performance. Edge detection is used on these features to reduce the number of calculations. The removal of shadows by combining color and contour features - for increased detection accuracy - is one of the advantages of the proposed method and it is a critical step in improving vehicle detection. The obtained results show that the proposed method can detect vehicles with an accuracy of 92.3%. This - in addition to the obtained F-measure and η values of 0.9264 and 0.8854, respectively - clearly state that the proposed method - which is based on deep learning and edge detection - creates a reasonable balance between speed and accuracy.

Traffic Scene Analysis and Classification using Deep Learning

“…On the other hand, since the results were obtained from real samples, they can be used for actual applications. Another vital criterion is accuracy, which is obtained by thefollowing relationship (19):…”

“…where, 𝜏 𝑖 (𝑥) can be an arbitrary function. This architecture builds upon the inception and ResNet architectures (19) to provide a new and improved architecture. The inception module is a significant change from sequential architectures.…”