Challenges in Object Detection
Object detection faces several challenges, including:
- Imbalanced Datasets: In many domains, negative samples (images without the object of interest) vastly outnumber positive samples, making it difficult to train accurate models.
- Domain Adaptation: Models trained on one type of data may not perform well on another due to differences in data distribution. Techniques like unsupervised domain adaptation are used to address this issue.
- Real-Time Processing: Achieving real-time performance while maintaining high accuracy is a significant challenge, especially in applications like autonomous driving and video surveillance.
Introduction to Object Detection Using Image Processing
Object detection is a crucial task in computer vision that involves identifying and locating objects within an image or video. This task is fundamental for various applications, including autonomous driving, video surveillance, and medical imaging. This article delves into the techniques and methodologies used in object detection, focusing on image processing approaches.
Table of Content
- Understanding Object Detection?
- Key Steps in Image Preprocessing
- Techniques in Object Detection Using Image Processing
- 1. Traditional Image Processing Techniques
- 2. Neural Network-Based Techniques
- Image Preprocessing Using OpenCV
- Applications of Object Detection
- Challenges in Object Detection