A Comprehensive Review on the Role of Deep Learning for Detecting and Identifying Obscured Objects

Human operators frequently encounter eye fatigue that typically comes from workload and sleep deprivation. This could affect the efficiency and performance of vigilance significantly. In safety-sensitive applications such as traffic systems, healthcare, and security, this issue is essential. To address this issue, there is a need for situational awareness and maintaining continuous monitoring of the workload of the person. To this end, advanced artificial intelligence (AI) methods, denoted by a deep learning (DL) based approach, can be used. Such methods are also supported by computer vision (CV) to detect and identify obscured objects. Combining CV with AI can overcome the challenges related to image/video detection in realistic scenarios, such as degradation due to the environmental conditions denoted by rain attenuation, smoke, fire, fog, and other realistic conditions and high visual noise. Therefore, DL algorithms can be effectively used to enhance detection through multi-scale feature extraction techniques and transformer-based models. The present paper reviews recent advancements in DL approaches and discusses the related work in CV toward obtaining effective object identification and detection under the challenges of realistic conditions.