Tools, techniques, datasets and application areas for object detection in an image: a review.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Object detection is a core computer vision task with applications in classification, counting, and monitoring.
• Significant research attention is dedicated to improving object detection accuracy and efficiency.
• Deep neural networks have substantially advanced object detection performance.

Purpose of the Study:

• To provide a comprehensive literature review of object detection techniques.
• To analyze traditional, two-stage, and one-stage object detection methodologies.
• To discuss datasets, annotation tools, and performance evaluation metrics for object detection.

Main Methods:

• A systematic literature review approach was employed.
• Analysis of various object detection architectures, optimization functions, and training strategies.
• Comparative analysis of different object detection techniques.

Main Results:

• Detailed analysis of traditional, two-stage, and one-stage object detection methods.
• Overview of dataset preparation, standard datasets, and annotation tools.
• Evaluation of key performance metrics used in object detection research.

Conclusions:

• Deep neural networks have significantly improved object detection capabilities.
• Understanding the differences in architecture, optimization, and training is crucial for selecting appropriate methods.
• Identified research challenges and future directions in the field of object detection.