CNN based 2D object detection techniques: a review - Lamichhane, Badri Raj; Srijuntongsiri, Gun; Horanont, Teerayut (Frontiers in Computer Science, 2025)
Significant advancements in object detection have transformed our understanding of everyday applications. These developments have been successfully deployed in real-world scenarios, such as vision surveillance systems and autonomous vehicles. Object recognition technologies have evolved from traditional methods to sophisticated, modern approaches. Contemporary object detection systems, leveraging high accuracy and promising results, can identify objects of interest in images and videos. The ability of Convolutional Neural Networks (CNNs) to emulate human-like vision has garnered considerable attention. This study provides a comprehensive review and evaluation of CNN-based object detection techniques, emphasizing the advancements in deep learning that have significantly improved model performance. It analyzes 1-stage, 2-stage, and hybrid approaches for object recognition, localization, classification, and identification, focusing on CNN architecture, backbone design, and loss functions. The findings reveal that while 2-stage and hybrid methods achieve superior accuracy and detection precision, 1-stage methods offer faster processing and lower computational complexity, making them advantageous in specific real-time applications.