  Nowadays, utilizing Advanced Driver-Assistance Systems (ADAS) has absorbed a
huge interest as a potential solution for reducing road traffic issues. Despite
recent technological advances in such systems, there are still many inquiries
that need to be overcome. For instance, ADAS requires accurate and real-time
detection of pedestrians in various driving scenarios. To solve the mentioned
problem, this paper aims to fine-tune the YOLOv5s framework for handling
pedestrian detection challenges on the real-world instances of Caltech
pedestrian dataset. We also introduce a developed toolbox for preparing
training and test data and annotations of Caltech pedestrian dataset into the
format recognizable by YOLOv5. Experimental results of utilizing our approach
show that the mean Average Precision (mAP) of our fine-tuned model for
pedestrian detection task is more than 91 percent when performing at the
highest rate of 70 FPS. Moreover, the experiments on the Caltech pedestrian
dataset samples have verified that our proposed approach is an effective and
accurate method for pedestrian detection and can outperform other existing
methodologies.
