Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review

Aziz, Lubna; Salam, Md. Sah Bin Haji; Sheikh, Usman Ullah; Ayub, Sara

doi:10.1109/access.2020.3021508

Cited by 101 publications

(34 citation statements)

References 232 publications

(314 reference statements)

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“…The feature maps of various sizes are evaluated for the recognition of small objects. Therefore, it is able to recognize relatively large-sized targets in an image [25,[30][31][32]. Finally, predictions of bounding boxes for each cell on the feature map are carried out in the network output using Equations (5)- (8), where the center coordinates and size of the obtained bounding box are described by Bx, By, Ba, Bb, respectively, as seen in Figure 5.…”

Section: Yolo Algorithmsmentioning

confidence: 99%

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings

Elsisi

Tran

Mahmoud

et al. 2021

Sensors

122

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Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper’s innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices.

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Section: Yolo Algorithmsmentioning

confidence: 99%

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings

Elsisi

Tran

Mahmoud

et al. 2021

Sensors

122

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“…In the further analysis, we used the

metric at the threshold

and the

metric averaged over ten equidistant thresholds

. Using mAP-based metrics in combination with cross-validation is a standard approach for performance evaluation and model comparison in object detection benchmarks [ 2 , 89 , 90 , 91 ].…”

Section: Honeybee Detectionmentioning

confidence: 99%

“…Object detection is a challenging computer vision task that is comprised of the localization and classification of objects [ 2 , 3 ] and thus helps to provide a proper understanding of an image. Traditional object detection models include informative region selection, the extraction of features, and classification.…”

Section: Introductionmentioning

confidence: 99%

“…State-of-the-art object detectors are mainly of two types: two-stage detectors and single-stage detectors [ 2 ]. The two-stage detectors generate regions of interests in the first stage and perform bounding box regression and object classification in the second stage.…”

Section: Introductionmentioning

confidence: 99%

“…Widely used single-stage detectors are the you look only once detector (YOLO) [ 73 ], the Single-shot multi-box detector (SSD) [ 74 ], and RetinaNET [ 75 ]. Even though the initial YOLOv1 detector is inferior to the SSD and RetinaNET detectors, the YOLO family of detectors has been continuously evolving and upgraded with the models improved in terms of accuracy and speed, resulting in the best balance between accuracy and execution time among the mentioned single-stage detectors [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Approximate GEMM Unit for Energy-Efficient Object Detection

Pilipović

Risojević

Božič

et al. 2021

Sensors

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Edge computing brings artificial intelligence algorithms and graphics processing units closer to data sources, making autonomy and energy-efficient processing vital for their design. Approximate computing has emerged as a popular strategy for energy-efficient circuit design, where the challenge is to achieve the best tradeoff between design efficiency and accuracy. The essential operation in artificial intelligence algorithms is the general matrix multiplication (GEMM) operation comprised of matrix multiplication and accumulation. This paper presents an approximate general matrix multiplication (AGEMM) unit that employs approximate multipliers to perform matrix–matrix operations on four-by-four matrices given in sixteen-bit signed fixed-point format. The synthesis of the proposed AGEMM unit to the 45 nm Nangate Open Cell Library revealed that it consumed only up to 36% of the area and 25% of the energy required by the exact general matrix multiplication unit. The AGEMM unit is ideally suited to convolutional neural networks, which can adapt to the error induced in the computation. We evaluated the AGEMM units’ usability for honeybee detection with the YOLOv4-tiny convolutional neural network. The results implied that we can deploy the AGEMM units in convolutional neural networks without noticeable performance degradation. Moreover, the AGEMM unit’s employment can lead to more area- and energy-efficient convolutional neural network processing, which in turn could prolong sensors’ and edge nodes’ autonomy.

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A robust multiclass 3D object recognition based on modern YOLO deep learning algorithms

Francies

Ata

Mohamed

2021

Concurrency and Computation

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A multiclass 3D object recognition has perceived a numerous evolution with respect to both accuracy and speed. This study introduces the implementation of modern YOLO algorithms (YOLOv3, YOLOv4, and YOLOv5) for multiclass 3D object detection and recognition. All YOLO algorithms have been tested according to a very large scaled dataset (Pascal VOC dataset). Performance evaluation has targeted the calculation of the following metrics; mAP (mean average precision), recall, F1-score, IOU (intersection over union), and the running time. Experimental results demonstrate that the YOLOv3 has targeted mAP of 77%, IOU of 0.41 and the total running time was almost 8 h. Moreover, in YOLOv4, it has targeted mAP of 55%, IOU of 0.035 and the total running time nearly 7 h. In addition, YOLOv5 has established the mAP of 48%, IOU of 0.045, and the total running time was about 3 h. Finally, a modified version of YOLOv5 has been proposed in the state-of-the-art of optimizing its hyperparameters and layering system. Accordingly, the mAP scored about 55% with 3 h running time. The final conclusions of this study have demonstrated that YOLOv3 has scored the highest recognition accuracy, however, the proposed modified YOLOv5 has scored the least processing time.

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Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review

Cited by 101 publications

References 232 publications

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings

An Approximate GEMM Unit for Energy-Efficient Object Detection

A robust multiclass 3D object recognition based on modern YOLO deep learning algorithms

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