Comparative study on object detection in visual scenes using deep learning

Object detection is a crucial aspect of computer vision, enabling machines to identify and locate various objects within images or videos. This paper provides an in-depth review of the subject, discussing its importance and applications across diverse fields, including autonomous vehicles, surveillance, augmented reality, healthcare, retail, and environmental monitoring.
The object detection framework is outlined, highlighting key steps such as image acquisition, preprocessing, feature extraction, object detection models, and post-processing. Deep learning techniques have significantly improved object detection, making it more accurate and faster. Various state-of-the-art models, such as YOLOv4, YOLOv5, and MobileNetV3, are presented with their respective performance metrics.
The paper also explores recent developments in object detection, including novel loss functions, neural architecture search (NAS), and advancements in handling challenging conditions like occlusions and low lighting. Despite the progress, there remain challenges in the field, such as improving object detection in complex environments.
Looking to the future, the paper predicts that object detection models will become more accurate and versatile, capable of handling challenging conditions and detecting a wider range of objects. Deep learning will continue to play a vital role in advancing object detection, leading to further breakthroughs in the field.
The provided references offer a comprehensive overview of the literature on object detection, making this paper a valuable resource for researchers and practitioners in the field.