Enhancing Object Detection with Mask R-CNN: A Deep Learning Perspective (original) (raw)
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Mask R-CNN: A Comparative Study on Improvements in Object Detection and Segmentation
Over the years there has been an increasing demand for image recognition as the world is moving towards a digital space. With the increasing demands, the application of Mask RCNN and expanded algorithms based on segmentation and YOLO have seen a major rise in the last 5 years. So the accuracy of the models has improved slightly since most projects take different approaches to the different datasets and have different metrics of Evaluation. By cross-referencing these approaches, a trend is observed that leads to higher success with the implementation of models using the hyperparameters and extra layers that have been added to the Mask RCNN in sequences. In this paper, the different approaches taken by different researchers are explored to understand how the implementations have progressed over the last half of the decade. In our research, we have studied analyzed 50 papers and found that the majority of papers were using the COCO dataset for training purposes with a specified set of hyper-parameters to measure the accuracy, performance, and memory consumption. The experiment findings were presented to suggest suitable RCNN architecture based on application or hardware attributes.
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2018
Object detection is the task of recognizing and localizing objects in an image. Object detection in images have many applications including object counting, Visual Search Engine, security, surveillance etc. Deep Learning based techniques for object detection are divided into two categories as region based approch and single shot approach. In this paper, region based approach technique Faster R-CNN was implemented using ResNET architecture of Convolutional Neural Network(CNN). The architecture of ResNET is modified to incorporate region proposal network to propose probable region of interest in an image and classification and regression network to detect and classify objects and their boundary in an image. The results were compared with Faster R-CNN based on VGG-16 on PASCAL VOC dataset . It was found that Faster R-CNN based on ResNET provides mean average precision of 0.78 which is better performance on PASCAL VOC dataset than VGG-16 architecture with mean average precision of 0.699.
Object Detection using Deep Learning Algorithm CNN
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.
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IRJET, 2022
Object detection is one of the most important achievements of deep learning and image processing since it finds and recognises objects in images. An object detection model may be trained to recognise and detect several objects, making it adaptable. Object detection had a large scope of interest even before deep learning approaches and modern-day image processing capabilities. It has become considerably more widespread in the present generation, thanks to the development of convolutional neural networks (CNNs) and the adaptation of computer vision technology. The latest wave of deep learning approaches to object detection gives up apparently limitless possibilities.
Deep Learning Approaches for Detecting Objects from Images: A Review
Advances in Intelligent Systems and Computing, 2018
Detecting objects from images is a challenging problem in the domain of computer vision and plays a very crucial role for wide range of real-time applications. The ever-increasing growth of deep learning due to availability of large training data and powerful GPUs helped computer vision community to build commercial products and services which were not possible a decade ago. Deep learning architectures especially convolutional neural networks have achieved state-of-the-art performance on worldwide competitions for visual recognition like ILSVRC, PASCAL VOC. Deep learning techniques alleviate the need of human expertise from designing the handcrafted features and automatically learn the features. This resulted into use of deep architectures in many domains like computer vision (image classification, visual recognition) and natural language processing (language modeling, speech recognition). Object detection is one such promising area immensely needed to be used in automated applications like self-driving cars, robotics, drone image analysis. This paper analytically reviews state-of-the-art deep learning techniques based on convolutional neural networks for object detection.
IEEE Access
Object detection is a fundamental but challenging issue in the field of generic image analysis; it plays an important role in a wide range of applications and has been receiving special attention in recent years. Although there are enomerous methods exist, an in-depth review of the literature concerning generic detection remains. This paper provides a comprehensive survey of recent advances in visual object detection with deep learning. Covering about 300 publications that we survey 1) region proposal-based object detection methods such as R-CNN, SPPnet, Fast R-CNN, Faster R-CNN, Mask RCN, RFCN, FPN, 2) classification/regression base object detection methods such as YOLO(v2 to v5), SSD, DSSD, RetinaNet, RefineDet, CornerNet, EfficientDet, M2Det 3) Some latest detectors such as, relation network for object detection, DCN v2, NAS FPN. Moreover, five publicly available benchmark datasets and their standard evaluation metrics are also discussed. We mainly focus on the application of deep learning architectures to five major applications, namely Object Detection in Surveillance, Military, Transportation, Medical, and Daily Life. In the survey, we cover a variety of factors affecting the detection performance in detail, such as i) a wide range of object categories and intra-class variations, ii) limited storage capacity and computational power. Finally, we finish the survey by identifying fifteen current trends and promising direction for future research. INDEX TERMS Object detection and recognition, deep learning, convolutional neural networks (CNN), and neural network.
Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy, and optimization function. In this paper, we provide a review of deep learning-based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely, the convolutional neural network. Then, we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection, and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network-based learning systems. in 2010, where he is currently pursuing the Ph.D.
Pertanika Journal of Science and Technology, 2020
The object detection system is a computer technology related to image processing and computer vision that detects instances of semantic objects of a certain class in digital images and videos. The system consists of two main processes, which are classification and detection. Once an object instance has been classified and detected, it is possible to obtain further information, including recognizes the specific instance, track the object over an image sequence and extract further information about the object and the scene. This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. MiniVGGNet is an architecture network used to train an object classification, and the data used for this purpose was collected from specific indoor environment building. For object detection, sliding wind...
Advances in Image and Video Processing, 2019
Object identification using deep learning in known environment gives a new dimension to the research area of computer vision based automation system. As it uses supervised learning technique using Convolution Neural Network (RCNN) it helps automation software tools and machines to detect and identify objects using vision based systems. One of RCNN technique known as Mask-RCNN has been applied in this proposed design and this paper presents a novel approach to object detection problem using Big Data storage for large set of features based data. Earlier work Faster Region-based CNN has led to the development of a state-of-the-art object detector termed as Mask R-CNN. Some samples of solid material objects used in refractory industry have been taken as input images. In our experiment the SIFT based features have been implemented and trained using filter and convolution operation. In addition to improved accuracy, pixel-level annotation (annotating bounding boxes is approximately an order of magnitude which is quicker to perform). The model is retrained to perform the detection of four types of metal objects, with the entire process of annotation and training for the new model per solid block. A key benefit of feature based Mask-RCNN approach is high precision (~94%) in classification and minimized feature points with SIFT key points.
A Review of Object Detection Models Based on Convolutional Neural Network
Advances in Intelligent Systems and Computing, 2020
Convolutional Neural Network (CNN) has become the stateof-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. Through this chapter, it has shown advancements in object detection models from R-CNN to latest RefineDet. It has also discussed the model description and training details of each model. Here, we have also drawn a comparison among those models.