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.
Highlights
Computer vision has been extensively researched in the area of object detection for industrial automation, consumer electronics, medical imaging, military, and video surveillance
It covers some specific problems in computer vision (CV) application areas, such as pedestrian detection, the military, crowd detection, intelligent transportation systems, medical imaging analysis, face detection, object detection in sports videos, and other domains. It provides an outlook on the available deep learning frameworks, application program Interface (API) services, and specific datasets used for object detection applications
(2) It cannot be implemented in real-time, as each test image needs around 47 s, and since the selective search method is a fixed algorithm, no learning happens at this rate and it leads to the generation of bad object region proposals
Summary
Computer vision has been extensively researched in the area of object detection for industrial automation, consumer electronics, medical imaging, military, and video surveillance. They require large image datasets for higher accuracy. CNNs require large labeled datasets to perform related tasks in computer vision, such as object classification, detection, object tracking, and recognition. With the advancement in technology and the availability of powerful graphics processing unit’s (GPU), deep learning has been employed on datasets; state-of-the-art results have been demonstrated by researchers in areas such as object classification, detection, and recognition. To perform both training and testing, deep learning requires powerful computational resources and larger datasets.
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