Real Time Smart Object Detection using Machine Learning

International Journal of Computer Applications, 2020

Deep learning based object detection has recently gained significant interest. This work focuses on real time object detection using two deep learning models named Faster Regional Convolution Neural Network (Faster-RCNN) and MobileNet Single Shot MultiBox Detector (MobileNet-SSD). An experiment is done using Python for programming, TensorFlow library for computing and OpenCV for computer vision. The Faster-RCNN and MobileNet-SSD models are trained using 400 images of four objects which are persons, watches, cell phones, and books. It is shown that for the images considered, Faster-RCNN can successfully detect these four objects with higher accuracy than MobileNet-SSD. Faster-RCNN also requires less time than MobilneNet-SSD for training the objects. However, Faster-RCNN model is slightly slower than MobileNet-SSD in real time object detection.

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 which 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, etc. In this paper, we provide a review on deep learning based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). 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.

Computer Science & Engineering: An International Journal, 2022

Object detection is a computer technique that searches digital images and videos for occurrences of meaningful subjects in particular categories (such as people, buildings, and automobiles). It is related to computer vision and image processing. Two well-studied aspects of identification are facial and pedestrian detection. Object detection is useful in a wide range of visual recognition tasks, including image retrieval and video monitoring. The object detection algorithm has been improved many times to improve the performance in terms of speed and accuracy. “Due to the tireless efforts of many researchers, deep learning algorithms are rapidly improving their object detection performance. Pedestrian detection, medical imaging, robotics, self-driving cars, face recognition and other popular applications have reduced labor in many areas.” It is used in a wide variety of industries, with applications range from individual safeguarding to business productivity. It is a fundamental compo...

Multimedia Tools and Applications

Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms—You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional Neural Networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss.

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.

2020

In computer vision, object detection is a very important, exciting and mind-blowing study. Object detection work in numerous fields such as observing security, independently/autonomous driving and etc. Deep-learning based object detection techniques have developed at a very fast pace and have attracted the attention of many researchers. The main focus of the 21st century is the development of the object-detection framework, comprehensively and genuinely. In this investigation, we initially investigate and evaluate the various object detection approaches and designate the benchmark datasets. We also delivered the wide-ranging general idea of object detection approaches in an organized way. We covered the first and second stage detectors of object detection methods. And lastly, we consider the construction of these object detection approaches to give dimensions for further research.

International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2025

In this research, the process of implementing deep learning models for real time object detection using YOLOv4 and SSD are explored. The models were trained and evaluated with dataset from publicly available datasets, transfer learning, preprocessing techniques, metrics like mean Average Precision (mAP) and Intersection over Union (IoU). In the case of YOLOv4 the detection accuracy was better than and the speed was faster than SSD, which was optimal for simpler, resource constrained environments. The models were confirmed to be practical via real time testing with webcam and performance was robust under different conditions. The study also explains the pros and cons of each model and makes recommendations on further improvement of efficiency in surveillance, healthcare, and smart systems such as object tracking, edge deployment, and adversarial robustness.

Digital Signal Processing

Object Detection is the task of classification and localization of objects in an image or video. It has gained prominence in recent years due to its widespread applications. This article surveys recent developments in deep learning based object detectors. Concise overview of benchmark datasets and evaluation metrics used in detection is also provided along with some of the prominent backbone architectures used in recognition tasks. It also covers contemporary lightweight classification models used on edge devices. Lastly, we compare the performances of these architectures on multiple metrics.

Artificial Intelligence Review, 2025

This comprehensive review looks into the recent developments in visual object detection, focusing on the transformative effect of deep learning (DL) technologies. In object detection, computer vision is a basic issue. This involves object detection and location in the video and image frames, which has notable advantages in robotics, autonomous driving, medical imaging, and surveillance. This review, therefore, presents a thorough integration analysis in visual object detection of the latest developments, providing both the historical context and state-of-the-art analysis. This review categorizes current methods into one-stage and two-stage frameworks, studying their architectural innovations, detection accuracy, computational speed, and deployment readiness. This review further scrutinizes the performance measures, emphasizes the inevitability of large-scale annotated datasets, and provides a curated overview of the widely used datasets in the field. Notable features include a discussion of practical applications and current research trends, and a comprehensive comparative analysis that compares models based on accuracy, speed, and trade-offs. A unique addition of this work is a thorough comparative analysis table that benchmarks traditional and modern models in terms of mean Average Precision (mAP), frames per second (FPS), advantages, limitations, and the coverage of transformer-based models and real-time deployments. The review's holistic approach provides significant insights for researchers and practitioners seeking to understand, benchmark, develop, or benchmark object detection systems.

Journal of emerging technologies and innovative research, 2018

In this paper, we implemented the image classification and object detection. This paper presents a deep learning approach for traffic light detection in adapting a single shot detection(SSD) approach and image classification of two categories of bicycle by retraining inceptionv3 model both using an open source tool called TensorFlow Object Detection API. We reviewed the current literature on convolutional object detection and tested the implementability of one of the methods and discovered that convolutional object detection is still evolving as a technology despite that convolutional object detection has outranked other object detection methods. To implement object detection and image classification there is free availability of datasets and pretrained networks it is possible to create a functional implementation of a deep neural network without access to specialist hardware. KEYWORDS-Object detection, Deep learning, Convolutional neural network, TensorFlow Object Detection API, SS...