A comprehensive review of object detection with deep learning

• Published in: Digital signal processing Vol. 132; p. 103812
• Main Authors: Kaur, Ravpreet, Singh, Sarbjeet
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.01.2023
• Subjects: Computer vision; Conventional methods; Deep convolutional neural network; Deep learning; Object detection; Deep learning; Computer vision; Deep convolutional neural network; Conventional methods; Object detection
• ISSN: 1051-2004; 1095-4333
• DOI: 10.1016/j.dsp.2022.103812

In the realm of computer vision, Deep Convolutional Neural Networks (DCNNs) have demonstrated excellent performance. Video Processing, Object Detection, Image Segmentation, Image Classification, Speech Recognition and Natural Language Processing are some of the application areas of CNN. Object detection is the most crucial and challenging task of computer vision. It has numerous applications in the field of security, military, transportation and medical sciences. In this review, object detection and its different aspects have been covered in detail. With the gradual increase in the evolution of deep learning algorithms for detecting objects, a significant improvement in the performance of object detection models has been observed. However, this does not imply that the conventional object detection methods, which had been evolving for decades prior to the emergence of deep learning, had become outdated. There are some cases where conventional methods with global features are superior choice. This review paper starts with a quick overview of object detection followed by object detection frameworks, backbone convolutional neural network, and an overview of common datasets along with the evaluation metrics. Object detection problems and applications are also studied in detail. Some future research challenges in designing deep neural networks are discussed. Lastly, the performance of object detection models on PASCAL VOC and MS COCO datasets is compared and conclusions are drawn.