AI-based object detection latest trends in remote sensing, multimedia and agriculture applications - Nawaz, Saqib Ali; Li, Jingbing; Bhatti, Uzair Aslam; Shoukat, Muhammad Usman; Ahmad, Raza Muhammad (Frontiers in Plant Science, 2022)
Object detection is a vital research direction in the field of machine vision, and deep learning. The object detection technique based on deep learning has achieved tremendous progress in feature extraction, image representation, classification, and recognition in recent years, due to this rapid growth of deep learning theory and technology. Scholars have proposed a series of methods for the object detection algorithm as well as improvement from the aspects of data processing, network structure, loss function and so on. In this paper, we introduce the characteristics of common datasets and key parameters of performance index evaluation, as well as the network structure and implementation methods of two-stage, single-stage and other improved algorithms are compared and analyzed. The latest improvement ideas of typical object detection algorithms based on deep learning are discussed and compared them from the aspects of data enhancement, a priori box selection, network model construction, prediction box selection, and loss calculation. Finally, combined with the existing challenges, the future research direction of typical object detection algorithms is surveyed.