Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review

TLDR

Object detection has advanced rapidly, moving from traditional methods to deep learning techniques that enable accurate localization of specific objects within complex scenes. This paper surveys recent advances and achievements in object detection with deep learning techniques. The survey covers a range of methods—from classic VJ and HOG to modern one‑shot and two‑shot detectors—alongside datasets, metrics, speed‑up strategies, and real‑time applications on GPU‑based embedded platforms. The review concludes by outlining promising future directions for object detection research.

Abstract

In recent years there has been remarkable progress in one computer vision application area: object detection. One of the most challenging and fundamental problems in object detection is locating a specific object from the multiple objects present in a scene. Earlier traditional detection methods were used for detecting the objects with the introduction of convolutional neural networks. From 2012 onward, deep learning-based techniques were used for feature extraction, and that led to remarkable breakthroughs in this area. This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques. Several topics have been included, such as Viola–Jones (VJ), histogram of oriented gradient (HOG), one-shot and two-shot detectors, benchmark datasets, evaluation metrics, speed-up techniques, and current state-of-art object detectors. Detailed discussions on some important applications in object detection areas, including pedestrian detection, crowd detection, and real-time object detection on Gpu-based embedded systems have been presented. At last, we conclude by identifying promising future directions.