Significant advances in object detection have been achieved through improved object representation and the use of deep neural network models. This paper examines more closely how object detection has evolved in the era of deep learning over the past years.

May 5, 2019 · Convolutional Neural Network (CNN) has become the state-of-the-art for object detection in image task. In this chapter, we have explained different state-of-the-art CNN based object detection models. We have made this review with categorization those detection models according to two different approaches: two-stage approach and one-stage approach.

Jan 30, 2022 · Object Detection: Locate the presence of objects with a bounding box and detect the classes of the located objects in these boxes. Object Recognition Neural Network Architectures created until now is divided into 2 main groups: Multi-Stage vs Single-Stage Detectors. Multi-Stage Detectors. Single-Stage Detectors.

Jan 1, 2023 · 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.

Dec 20, 2019 · Convolutional neural networks (CNN), first introduced by Fukushima [17] in 1998, have wide applications in activity recognition [18, 19], sentence classification [20], text recognition [21], face recognition [22], object detection and localization [23, …

Nov 20, 2023 · Object detection (OD) is growing rapidly due to the rebirth of convolution neural networks. The deep CNNs are capable to learn prominent-feature representations of images due to their typical hierarchical architecture, and hence, it offers a fast, rapid, and accurate way to predict the position of objects within the image.

Dec 4, 2019 · In this chapter, we present a brief overview of the recent development in object detection using convolutional neural networks (CNN). Several classical CNN-based detectors are presented.

Jul 8, 2020 · We analyze and focus on the various state-of-the-art convolutional neural networks serving as a backbone in object detection models. We test and evaluate them in the common datasets and benchmarks up-to-date. We Also outline the main features of each architecture.

In this paper, we develop a new approach for detecting multiple objects from images based on convolutional neural networks (CNNs). In our model, we first adopt the edge box algorithm to generate region proposals from edge maps for each image, and perform forward passing of all the propos-als through a fine-tuned CaffeNet model.

At present, object detection algorithms are mainly divided into two types, tradi- tional object detection algorithms based on image processing and object detec- tion algorithms based on convolutional neural networks. In 2014, Girshick et al. proposed R-CNN [6] on this basis.