Paper Contents
Abstract
Highway safety is a critical concern due to the increasing number of accidents involving pedestrians and stray animals. These incidents often result in severe injuries, fatalities, and significant vehicular damage. The integration of intelligent systems into vehicles and road infrastructure is crucial to addressing these safety challenges. This paper presents an advanced object detection system specifically designed to enhance pedestrian and animal safety on highways using deep learning and computer vision techniques.The proposed system leverages state-of-the-art convolutional neural networks (CNNs) and pre-trained object detection models such as YOLO (You Only Look Once) and Faster R-CNN, which offer real-time detection capabilities with high accuracy. These models are trained and fine-tuned on datasets containing annotated images of pedestrians and various animal species commonly found on or near highways. The system processes live video streams from vehicle-mounted cameras or roadside surveillance units, enabling continuous monitoring of the roadway. When a pedestrian or animal is detected, the system triggers alerts that can either be used to warn the driver or to activate automatic braking mechanisms in advanced driver-assistance systems (ADAS).In addition to detection, the system incorporates location tracking and motion prediction to estimate the trajectory of the detected object, allowing for early warning and improved collision avoidance. Techniques such as Histogram of Oriented Gradients (HOG) and Haar cascades were initially explored for comparison, but deep learning-based models significantly outperformed traditional methods in terms of detection accuracy and speed, especially under varying lighting and weather conditions.The system was tested on real-world highway video datasets and custom simulation environments. The experimental results demonstrate high detection precision and recall, with robust performance in day and night scenarios, fog, and partial occlusions. False positives and negatives were minimized through data augmentation, transfer learning, and careful selection of hyperparameters. Furthermore, the implementation was optimized for deployment on embedded hardware such as NVIDIA Jetson boards, making it suitable for real-time applications in smart vehicles.This work contributes to the growing field of intelligent transportation systems (ITS) and supports the vision of safer, smarter highways. By accurately detecting and tracking pedestrians and animals in real-time, the proposed system can significantly reduce the occurrence of road accidents, improve driver reaction times, and save lives. Future work will focus on integrating thermal imaging for improved night-time performance and expanding the model to detect a broader range of objects and hazardous conditions.
Copyright
Copyright © 2025 Abhishek Pandey. This is an open access article distributed under the Creative Commons Attribution License.
