Object Detection and Recognition in Real Time Using Deep Learning for Visually Impaired People

Computer vision (CV) and human-computer interaction (HCI) are essential in many technological fields. Researchers in CV are particularly interested in real-time object detection techniques, which have a wide range of applications, including inspection systems. In this study, we design and implement real-time object detection and recognition systems using the single-shoot detector (SSD) algorithm and deep learning techniques with pre-trained models. The system can detect static and moving objects in real-time and recognize the object's class. The primary goals of this research were to investigate and develop a real-time object detection system that employs deep learning and neural systems for real-time object detection and recognition. In addition, we evaluated the free available, pre-trained models with the SSD algorithm on various types of datasets to determine which models have high accuracy and speed when detecting an object. Moreover, the system is required to be operational on reasonable equipment. We tried and evaluated several deep learning structures and techniques during the coding procedure and developed and proposed a highly accurate and efficient object detection system. This system utilizes freely available datasets such as MS Common Objects in Context (COCO), PASCAL VOC, and Kitti. We evaluated our system's accuracy using various metrics such as precision and recall. The proposed system achieved a high accuracy of 97% while detecting and recognizing real-time objects.

Over the past few years, deep learning has played a tremendous role in real time object detection, recognition and classification. Today, algorithms use deep learning not only for simple object detection but have also paved way for vehicle detection that is complex situation such as Indian roads. Different deep learning models can learn features of varied complexity and perform according to surrounding environment in which they are deployed. In this paper a comparison of six models is performed for detection of vehicles on roads that are unstructured or unconstrained. Till date, deep learning algorithms are being used for vehicle (car, bus, truck, motorcycle etc.) detection. A dataset based on Indian road scenario has been used for training on models: faster RCNN and Single Shot Detector (SSD) combined with convolution classifiers mobilenet, resnet and inceptionnet. The six models are compared by evaluating mean average precision (mAP) and classification time complexity for IDD dataset. The observations suggested faster RCNN Resnet50 resolves the trade-off of time and accuracy. Whereas SSD mobilenet V1 takes 31.89% less time than faster RCNN resnet101. On the flipped side, faster RCNN resnet101is 34.38% more accurate than SSD mobilenet v1.

For real-time object detection and speech alarms, Research offers a sophisticated blind aid solution that smoothly combines the YOLO algorithm with Open CV's DNN (Deep Neural Network) module. The main objective is to improve the safety and freedom of people with visual impairments by offering quick object recognition and audio feedback. The YOLO method, which is tuned for real-time inference, is used by the system to precisely recognize objects after using a webcam to record live video input. Then, using audio description, it produces speech notifications that provide crucial details about the things it has spotted. Because of its exceptional versatility, the research can provide speech outputs in the user's preferred language, increasing its usability and accessibility. Its versatility is further demonstrated by its capacity to precisely handle a variety of object classes, making it a priceless tool for greatly enhancing the lives of those who are blind or visually impaired. Challenges in real-time object detection include occlusion, scale variations, and cluttered environments. Researchers must navigate the trade-offs between accuracy and speed. Real- time object detection is pivotal in computer vision, enabling intelligent systems across diverse applications. Real-time object detection systems designed for assistive technologies are becoming increasingly important in empowering individuals with visual impairments. The research integrates the YOLO algorithm with OpenCV’s DNN module to deliver a highly responsive blind-aid solution capable of capturing live video through a webcam and processing it instantly. YOLO’s architecture, optimized for speed and accuracy, enables the system to detect and classify multiple objects within each video frame, even in dynamic or unpredictable environments. Once an object is identified, the system generates immediate speech output, providing descriptive audio alerts in the user’s preferred language, thus enhancing accessibility and user experience. This multilingual capability makes the solution versatile across different regions and cultures. Additionally, the system supports a wide range of object categories such as vehicles, household items, and obstacles helping visually impaired users navigate daily surroundings more safely and independently. However, the implementation faces challenges, including handling occlusions, varying object scales, low-light conditions, and cluttered backgrounds. These factors require careful balancing between detection accuracy and processing speed to maintain real-time performance. Despite these challenges, the integration of advanced deep learning models with user-friendly audio interfaces demonstrates significant potential to improve mobility, safety, and overall quality of life for people with visual impairments.

Recently real-time detection, and recognition of an object of interest are becoming vital tasks in visual data processing and computer vision. Various models have been deployed to implement object detection and tracking in multiple fields. However, conventional classifiers are often faced with challenging tasks that visual frames come distorted due to overlapping, camera motion blur, changing subject appearances, and environmental variations. Models using OpenCV-based HAAR feature-based cascade classifiers, without integrating any error minimizing object detection algorithm, were unable to accurately detect an object and track it in a changing environment. Therefore, developing an embedded powerful framework for realtime object detection and recognition becomes more of a vital need for future implementation in various fields. This study presents a powerful technique for a real-time detector that utilizes integrated Deep Learning Neural Networks (DNN) for optimal computational accuracy. Deploying such a framework will ensure the flexibility and reliability of the detector by eliminating the sources of distortion previously mentioned. The model relies on integrating the ImageAI deep learning libraries and You Only Look Once (YOLO-v3) object detection method with a DarkNet53 architecture. The algorithm was trained using the TensorFlow framework to ensure accurate data processing. This paper targets one vital component of our long-term project of developing a multi-agent system, as the proposed model is to be implemented in autonomous agents for the detection of landmines, ocean debris, and wildlife beside environmental scanning missions. In this study the performance of the model has been assessed through detecting and collecting tennis balls as a preliminary test for real-world applications. The model was able to approach the desirable result of surpassing the accuracy of conventional detectors.

Herbarium specimen scans constitute a valuable source of raw data. Herbarium collections are gaining interest in the scientific community as their exploration can lead to understanding serious threats to biodiversity. Data derived from scanned specimen images can be analyzed to answer important questions such as how plants respond to climate change, how different species respond to biotic and abiotic influences, or what role a species plays within an ecosystem. However, exploiting such large collections is challenging and requires automatic processing. A promising solution lies in the use of computer-based processing techniques, such as Deep Learning (DL). But herbarium specimens can be difficult to process and analyze as they contain several kinds of visual noise, including information labels, scale bars, color palettes, envelopes containing seeds or other organs, collection-specific barcodes, stamps, and other notes that are placed on the mounting sheet. Moreover, the paper on which the specimens are mounted can degrade over time for multiple reasons, and often the paper's color darkens and, in some cases, approaches the color of the plants.

The process of object detection utilizing deep learning is one of the most important deep learning applications and computer vision techniques, where one can learn image features in normal weather conditions and different rain conditions. Therefore, a deep convolutional neural network (DCNN) has become more important for object detection. Rain is a common and maj or factor in degrading image quality and decreasing object detection reliability. The main aim of this work is to remove rain streaks to get high reliability in detection process and decrease the error rate, in normal conditions and different rain conditions (light, medium and heavy). Firstly, the quality of the images is improved and removed rain streaks by de-raining algorithm that use the Deep Detail Network (DDN) method. Then the way deep learning is the main object detector, through use the YOLO to detect objects and determine its type. YOLOv3 and tiny-YOLOv3 have been determine from the literature review as the most suitable and efficient algorithms for detecting objects in real time after improving the image quality. The performance of these algorithms has been calculated and compared with each other. To evaluate the effectiveness of the devised approach (De-raining+YOLOv3), Fl-score, Recall, and Precision were computed. Using the proposed method combined from DDN with YOLOv3 technique (De-raining+YOLOv3), the mean of Fl-score of 95.02%, Recall of 97.22%, and a Precision of 92.92% were attained. Our presented approach is more resilient and accurate in object detection under rain conditions according to the findings of the results of the experiments. It is considered the best way for object detection under rain conditions with high reliability.

Deep neural networks (DNNs) play an important role in our daily lives, from aiding us in menial tasks to solving world issues such as cancer cell detection. However, few pieces of research have been conducted using DNNs and deep learning models as a medium to help classify and detect trash, in efforts to solve our global waste crisis. This is because current DNNs struggle to be both efficient and accurate while detecting indistinct objects such as waste. To address this issue, this work focuses on YOLO-Green, a novel real-time object detection model designed specifically for trash detection. The model is trained on a dataset gathered from real-world trash divided into seven of the most common types of solid waste. With only 100 epochs of training, YOLO-Green achieves an outstanding mAP of 78.04%, FPS of 2.72, while retaining a model size of only 117 MB. Based on the original object detection of YOLOv4, YOLO-Green exceeds YOLOv4 and other popular deep learning models in both its accuracy and efficiency, while maintaining a relatively small model size. Ultimately, this study sheds a positive light on the potential of using deep learning models as an alternative to manual waste management.

In computer vision, significant advances have been made in recent years on object recognition and detection with the rapid development of deep learning, especially deep convolutional neural networks (CNN). The majority of deep learning methods for object detection have been developed for large objects and their performances on small-object detection are not very good. This paper contributes to research in low-resolution small-object detection by evaluating the performances of leading deep learning methods for object detection using a common dataset, which is a new dataset for bird detection, called Little Birds in Aerial Imagery (LBAI), created from real-life aerial imagery data. LBAI contains birds with sizes ranging from 10px to 40px. In our experiments, some of the best deep learning architectures were implemented and applied to LBAI, which include object detection techniques such as YOLOv2, SSH, and Tiny Face, in addition to small instance segmentation techniques including U-Net and Mask R-CNN. Among the object detection methods, experimental results demonstrated that SSH performed the best for easy cases, whereas Tiny Face performed the best for hard cases, i.e. where a cluttered background makes detecting birds difficult. Among small instance segmentation methods, experimental results revealed U-Net achieved slightly better performance than Mask R-CNN.

Object Detection has been one of the areas of interest of research community for over years and has made significant advances in its journey so far. There is a tremendous scope in the applications that would benefit with more innovations in the domain of object detection. Rapid growth in the field of machine learning has complemented the efforts in this area and in the recent times, research community has contributed a lot in real time object detection. In the current work, authors have implemented real time object detection and have made efforts to improve the accuracy of the detection mechanism. In the current research, we have used ssd_v2_inception_coco model as Single Shot Detection models deliver significantly better results. A dataset of more than 100 raw images is used for training and then xml files are generated using labellimg. Tensor flow records generated are passed through training pipelines using the proposed model. OpenCV captures real-time images and CNN performs convolution operations on images. The real time object detection delivers an accuracy of 92.7%, which is an improvement over some of the existing models already proposed earlier. Model detects hundreds of objects simultaneously. In the proposed model, accuracy of object detection significantly improvises over existing methodologies in practice. There is a substantial dataset to evaluate the accuracy of proposed model. The model may be readily useful for object detection applications including parking lots, human identification, and inventory management.

Sensor devices are regularly used on unmanned aerial vehicles (UAVs) as reconnaissance and intelligence gathering systems and as support for front line troops on operations. This platform provides a wealth of sensor data and has limited computational power available for processing. The objective of this work is to detect and identify objects in real-time, with a low power footprint so that it can operate on a UAV. An appraisal of current computer vision methods is presented, with reference to their performance and applicability to the objectives. Experimentation with real-time methods of background subtraction and motion estimation was carried out and limitations of each method described. A new, assumption free, data driven method for object detection and identification was developed. The core ideas of the development were based on models that propose that the human vision system analyses edges of objects to detect and separate them and perceives motion separately, a function which has been modelled here by optical flow. The initial development in the temporal domain combined object and motion detection in the analysis process. This approach was found to have limitations. The second iteration used a detection component in the spatial domain that extracts texture patches based on edge contours, their profile, and internal texture structure. Motion perception was performed separately on the texture patches using optical flow. The motion and spatial location of texture patches was used to define physical objects. A clustering method is used on the rich feature set extracted by the detection method to characterise the objects. The results show that the method carries out detection and identification of both moving and static objects, in real-time, irrespective of camera motion.

In 2021, 42% of bridges in the United States were used beyond their design life, incurring a repair cost of approximately $125 billion. To reduce future repair costs of bridges, novel inspection methods have been recently proposed that use unoccupied aerial vehicles to inspect bridge defects, where a computer vision-based model processes the data to assess bridge condition. Although this technology can provide significant advantages in decreasing inspection costs and labor, inspection accuracy relies on the quality of images and the computer vision model. Therefore, this study develops a multi-task deep learning framework that combines object detection and semantic segmentation models to detect important structural details and quantify corrosion level in steel bridges with high precision and speed (i.e., inference time). The framework leverages high-performing computing and high-definition images to minimize resizing effects on model accuracy. For each module, two different deep learning architectures were developed and compared in terms of accuracy and inference time. The results show the efficiency of deep learning models for both tasks. For an intersection over a union threshold of 50%, the object detection models of YOLOv8 and SSD RetinaNet50 achieve mean average precision of 96.6% and 85.2%, respectively. In addition, the segmentation models of YOLOv8 and DeeplabV3+ achieved an F1 score of 61.5% and 28%, respectively.

Object detection and recognition is an essential task in computer vision with numerous real-world applications such as surveillance, self-driving cars, and robotics. In recent years, deep learning-based approaches have significantly improved the accuracy and speed of object detection and recognition. The You Only Look Once version 3 (YOLOv3) algorithm is a popular deep learning-based approach that can detect and recognize objects in real-time. The Common Objects in Context (COCO) dataset is a large-scale dataset with over 330,000 labeled images and more than 2.5 million object instances, making it a popular choice for object detection and recognition tasks. In this paper, we propose a deep learning-based approach for real-time object detection and recognition using the YOLOv3 architecture and COCO dataset. We evaluate our approach based on several performance metrics, including mean average precision (mAP), frames per second (FPS), total object detection time, object detection accuracy, false positive rate, number of detected objects, and mean intersection over union (mIoU). Our results show that our approach achieves a mean average precision of 0.76 on the COCO dataset and a real-time performance of 40 frames per second on a single GPU. Additionally, our approach achieves an object detection accuracy of 93.5%, a false positive rate of 6.5%, and a mean intersection over union of 0.65. Our proposed approach shows promising results for real-time object detection and recognition and can be applied to various real-world applications.

Detection of objects precisely in video surveillance system is vital for many applications such as abnormal activity detection, congestion analysis, person identification, military fields. For detection of objects, background subtraction and temporal detection methods are used. In background subtraction method moving and static objects which enters into a camera viewing region has been detected. In this method reference frame will be captured during camera initialization. It uses a reference frame as background, detects present frame and takes the difference between two frames. In this paper objects are detected in multiple regions separately from single camera instead of using multiple cameras to monitor different objects in different regions. And also user will have control to select required single or multiple regions to detect the object. This will reduces the overall cost of the system and increases the processing speed of the system if we choose a particular region. Accuracy of the object detection in different regions increases since it allows using different parameters like thresholding separately. So that it monitors different activities at the same time. Also detection of objects based on comparison, object of interest from stored data base is presented using single static camera view with good real time performance.

The real-time detection of objects and persons in their approach remains vital for applications that need safety measures security protocols and efficiency in autonomous systems and surveillance operations and industrial automation. The proposed research creates a durable real-time detection system through sensor combination using ultrasonic along with infrared and LiDAR and radar sensors and computer vision-based sensors to enhance detection accuracy and response times in changing environmental scenarios. The method starts by processing data gathered from arrays of multiple sensors through optimized filtering techniques and extraction methods before machines learn to classify objects while reducing false detection instances. The assessment of performance takes place in different environmental conditions to track both detection speed along with accuracy levels and system reliability. Sensor fusion enables real-time detection systems to perform better because it reduces the impact of environmental changes and sensor distortions. System capabilities increase in different operational areas when researchers demonstrate pre-processing optimization works alongside intelligent data processing systems. The research findings combine autonomous navigation systems with smart surveillance technology together with safety protocols for industrial security which enable real-time scalability and reduced risk elements. Research demonstrates a specific achievement through its analysis that machines learn better when combined with multi-sensor integration over single-sensor models which improves system reliability when used in real-world applications. Multi-sensor arrays and advanced processing techniques generate enhanced system responsiveness alongside increased adaptability and reliability thus improving detection systems within industrial security measures and automation operations.

Object detection algorithms using convolutional neural networks (CNNs) achieve high detection accuracy, but it is challenging to realize real-time object detection due to their high computational complexity, especially on resource-constrained mobile platforms. In this article, we propose an algorithm-hardware co-optimization approach to designing a real-time object detection system. We first develop a compact object detection model based on a binarized neural network (BNN), which employs a new layer structure, the DenseToRes layer, to mitigate information loss due to deep quantization. We also propose an efficient object detection processor that runs object detection with high throughput using limited hardware rescources. We develop a resource-efficient processing unit supporting variable precision with minimal hardware overheads. Implemented in field-programmable gate array (FPGA), the object detection processor achieves 64.51 frames/s throughput with 64.92 mean average precision (mAP) accuracy. Compared to prior FPGA-based designs for object detection, our design achieves high throughput with competitive accuracy and lower hardware implementation costs.