Survey and Performance Analysis of Deep Learning Based Object Detection in Challenging Environments.

Area of Science:

• Computer Vision
• Deep Learning
• Artificial Intelligence

Background:

• Generic object detection models excel with rich data but struggle in real-world challenging environments.
• Challenging environments include occlusions, low-light conditions, and background merging, significantly degrading performance.
• Existing research lacks a consolidated reference for deep learning-based object detection in these difficult scenarios.

Purpose of the Study:

• To provide the first comprehensive overview of state-of-the-art deep learning approaches for object detection in challenging environments.
• To analyze the performance of these approaches quantitatively and qualitatively.
• To discuss available challenging datasets and benchmark current algorithms.

Main Methods:

• Literature review of recent deep learning-based object detection techniques for challenging environments.
• Quantitative and qualitative performance analysis of reviewed approaches.
• Benchmarking of state-of-the-art generic object detection algorithms on three challenging datasets.

Main Results:

• Identified and analyzed various deep learning strategies designed to overcome challenges like occlusion and low light.
• Presented a comparative performance analysis of different object detection methods in adverse conditions.
• Highlighted the limitations of current generic object detection algorithms in complex real-world scenarios.

Conclusions:

• A significant gap exists in consolidated knowledge regarding object detection in challenging environments.
• Current state-of-the-art methods show promise but require further development for robust real-world application.
• Future research should focus on improving model robustness and addressing identified shortcomings.