Object Detection using Deep Learning: A Review

Anushka,; Arya, Chandrakala; Tripathi, Amrendra; Singh, Prabhishek; Diwakar, Manoj; Sharma, Kanika; Pandey, Happy

doi:10.1088/1742-6596/1854/1/012012

Cited by 20 publications

(4 citation statements)

References 7 publications

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“…Considering that these segmented regions would overlap between them, then every image is processed more than once from the CNN. The most widely used techniques to date are always based on deep learning and include R-FCNs (Region-based Fully Convolutional Networks) [ 34 ], RetinaNet [ 35 ], SSD (Single-Shot MultiBox Detector) [ 36 ], and DSSD (Deconvolutional Single-Shot Detector) [ 37 , 38 ].…”

Section: Object Detection State-of-the-artmentioning

confidence: 99%

Design and Characterization of a Powered Wheelchair Autonomous Guidance System

Gallo,

Shallari,

Carratù

et al. 2024

Sensors

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The current technological revolution driven by advances in machine learning has motivated a wide range of applications aiming to improve our quality of life. Representative of such applications are autonomous and semiautonomous Powered Wheelchairs (PWs), where the focus is on providing a degree of autonomy to the wheelchair user as a matter of guidance and interaction with the environment. Based on these perspectives, the focus of the current research has been on the design of lightweight systems that provide the necessary accuracy in the navigation system while enabling an embedded implementation. This motivated us to develop a real-time measurement methodology that relies on a monocular RGB camera to detect the caregiver’s feet based on a deep learning method, followed by the distance measurement of the caregiver from the PW. An important contribution of this article is the metrological characterization of the proposed methodology in comparison with measurements made with dedicated depth cameras. Our results show that despite shifting from 3D imaging to 2D imaging, we can still obtain comparable metrological performances in distance estimation as compared with Light Detection and Ranging (LiDAR) or even improved compared with stereo cameras. In particular, we obtained comparable instrument classes with LiDAR and stereo cameras, with measurement uncertainties within a magnitude of 10 cm. This is further complemented by the significant reduction in data volume and object detection complexity, thus facilitating its deployment, primarily due to the reduced complexity of initial calibration, positioning, and deployment compared with three-dimensional segmentation algorithms.

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Section: Object Detection State-of-the-artmentioning

confidence: 99%

Design and Characterization of a Powered Wheelchair Autonomous Guidance System

Gallo,

Shallari,

Carratù

et al. 2024

Sensors

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“…You Only Look Once (YOLO) is a viral and widely used algorithm [7]. You Look Only Once (YOLO) is the first one-stage model, which was proposed in 2015 [8]. YOLO works by taking a photo and dividing it into an SxS grid.…”

Section: A You Only Look Once (Yolov7)mentioning

confidence: 99%

Comparative Analysis of Yolo and SSD

2023

IRJMETS

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Object detection is a computer vision technique. It plays a significant role in healthcare, autonomous vehicles, facial recognition and traffic management system, license plate recognition and so on. Our paper will focus on the existing deep learning techniques based on convolutional neural networks and how these techniques are different from each other. These techniques include single stage models such as -YOLOV7, YOLOV8, and SSD. Our paper will compare these techniques to draw meaningful conclusions. With the help of these single stage models, we are implementing vehicle detection systems and then comparing these model's performance. In comparative analysis, we are using Coco dataset, we evaluated the performance of these techniques based on parameters such as: mean average precision, inference time and recall time.

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“…The classification of existing computer vision-based object detection algorithms primarily falls into two categories (Arya et al, 2021 ): two-stage detection algorithms, such as R-CNN, SPP-Net, Fast R-CNN, Faster R-CNN, and FPN, and one-stage detection algorithms, including the YOLO series, SSD, and RetinaNet.…”

Section: Related Workmentioning

confidence: 99%

Recognition new energy vehicles based on improved YOLOv5

Kong

Zhou

et al. 2023

Front. Neurorobot.

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In the field of Intelligent Traffic Systems (ITS), vehicle recognition is a hot research topic. Although different types of vehicles can already be recognized, further identification and statistics of new energy and fuel vehicles in unknown and complex environments remain a challenging task. In this paper, we propose a New Energy Vehicle Recognition and Traffic Flow Statistics (NEVTS) approach. Specifically, we first utilized the You Only Look Once v5 (YOLOv5) algorithm to detect vehicles in the target area, in which we applied Task-Specific Context Decoupling (TSCODE) to decouple the prediction and classification tasks of YOLOv5. This approach significantly enhanced the performance of vehicle detection. Then, track them upon detection. Finally, we use the YOLOv5 algorithm to locate and classify the color of license plates. Green license plates indicate new energy vehicles, while non-green license plates indicate fuel vehicles, which can accurately and efficiently calculate the number of new energy vehicles. The effectiveness of the proposed NEVTS in recognizing new energy vehicles and traffic flow statistics is demonstrated by experimental results. Not only can NEVTS be applied to the recognition of new energy vehicles and traffic flow statistics, but it can also be further employed for traffic timing pattern extraction and traffic situation monitoring and management.

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Object Detection using Deep Learning: A Review

Cited by 20 publications

References 7 publications

Design and Characterization of a Powered Wheelchair Autonomous Guidance System

Design and Characterization of a Powered Wheelchair Autonomous Guidance System

Comparative Analysis of Yolo and SSD

Recognition new energy vehicles based on improved YOLOv5

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