PED-AI: Pedestrian Detection for Autonomous Vehicles using YOLOv5

Abstract

Pedestrian detection is crucial for autonomous vehicles, surveillance, and pedestrian safety. This abstract introduces a novel pedestrian detection method using the YOLOv5 algorithm, known for its real-time object detection prowess. The approach aims to enhance pedestrian detection accuracy across diverse lighting conditions. Methodologically, the process involves data preparation, YOLOv5 model training, and subsequent evaluation. The architecture of YOLOv5, which employs anchor boxes and a single-pass convolutional neural network, allows for quick and accurate pedestrian identification. YOLOv5's design, which includes anchor boxes and a single-pass convolutional neural network, enables speedy and accurate pedestrian recognition. Study tests confirm the efficacy of the YOLOv5-based approach. In the first scenario, the model detected pedestrians in daylight with 75% accuracy, but it also produced 11 false negatives or a 25% miss. Although Scenario 2's accuracy was higher at 85%, there were still 11 false negatives, which suggested that there was a persistent detection gap. In spite of these outcomes, the YOLOv5 model demonstrates the possibility of accurate pedestrian detection in real-world settings. While it greatly improves applications like self-driving cars and pedestrian safety, lowering false negatives remains a primary goal for increasing overall accuracy. The investigation's findings show that YOLOv5 can function in a variety of lighting conditions, but also highlight the necessity for further work in order to meet stringent detection requirements.