Object Detection using Image Processing

TL;DR

This paper presents a Python-based object detection system using Haar Cascade algorithms to improve UAV border security by reducing detection errors and processing time for surveillance and target identification.

Contribution

It develops an OpenCV-Python implementation of Haar Cascade for UAV object detection, enhancing detection accuracy and speed for military surveillance applications.

Findings

Reduced processing time in object detection

Effective detection of humans and ground targets

Potential to prevent UAV collisions

Abstract

An Unmanned Ariel vehicle (UAV) has greater importance in the army for border security. The main objective of this article is to develop an OpenCV-Python code using Haar Cascade algorithm for object and face detection. Currently, UAVs are used for detecting and attacking the infiltrated ground targets. The main drawback for this type of UAVs is that sometimes the object are not properly detected, which thereby causes the object to hit the UAV. This project aims to avoid such unwanted collisions and damages of UAV. UAV is also used for surveillance that uses Voila-jones algorithm to detect and track humans. This algorithm uses cascade object detector function and vision. train function to train the algorithm. The main advantage of this code is the reduced processing time. The Python code was tested with the help of available database of video and image, the output was verified.