A robust approach for industrial small-object detection using an improved faster regional convolutional neural network

Saeed, Faisal; Ahmed, Muhammad; Gul, Malik Junaid Jami; Hong, Kim Jeong; Paul, Anand; Kavitha, Muthu Subash

doi:10.1038/s41598-021-02805-y

Cited by 25 publications

(12 citation statements)

References 27 publications

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“…Services offered through the IoT will allow smart objects to become malleable participants in ideological agendas and economical operations. They might share resources and work together to pass things around (Din et al, 2018;Saeed et al, 2021). Each sensing device communicates with the others.…”

Section: Introductionmentioning

confidence: 99%

Smart Decision-Making and Communication Strategy in Industrial Internet of Things

Gunasekaran¹,

Kumar

Kaladevi

et al. 2023

IEEE Access

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Smart machine-machine (M2M) interactions, such as those enabled by the Internet of Things (IoT), have enabled people and machines to communicate and make decisions together. Furthermore, these systems have become increasingly important in the commercial and industrial sectors over the previous two decades. The Industrial Internet of Things (IIoT) is a smart system comprising engineering equipment which can connect to one another to improve manufacturing operations. This task would become more complicated if the amount of energy used by the IIoT ecosystems, as well as the amount of network traffic they generate, increased dramatically. Consequently, decision-making processes during communication are essential for autonomous interaction in critical IoT infrastructure. Smart factories employ communication technology to track and gather information in real-time to enhance the output, effectiveness, and predictability while lowering the overall cost of vital operations. In this context, Industry 4.0 not only limits to addresses the issues of integrating technologies, but it also focuses on data collection, dissemination, utilization, and organization and also improves the delivery of the solution or services quicker with more sustainability. This study intends to create an NF-based communication system for IIoT platforms to leverage those benefits. The proposed model includes smart decision-making procedures to deal with communication issues. Compared with the many methods already in use, the suggested mechanism's functional viability in the automated system is found to be optimal. Outcomes from simulations reveal that the suggested method has improved the accuracy and communication reliability of the IIoT platforms in comparison with the previous methods. Aside from these, the suggested model keeps the throughput of the local automation unit at 96.03% and the throughput of the production hall at 95.58% on average while maintaining the lowest average PLR of about 26.48% across different data rates.

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Section: Introductionmentioning

confidence: 99%

Smart Decision-Making and Communication Strategy in Industrial Internet of Things

Gunasekaran¹,

Kumar

Kaladevi

et al. 2023

IEEE Access

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show abstract

“…Also, deep learning based object detection algorithms can be well suited for many industrial applications such as quality monitoring, classification counting, and security management. Therefore, the integration of deep learning-based object detection into IIoT systems can enhance the automation and intelligence of IIoT systems greatly [4].…”

Section: Introductionmentioning

confidence: 99%

PG-YOLO: A Novel Lightweight Object Detection Method for Edge Devices in Industrial Internet of Things

et al. 2022

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With the rapid development of Industrial Internet of Things (IIoT) technology, video surveillance devices and video data in IIoT environments are massively growing and increasingly important. Deploying rapidly evolving deep learning-based object detection algorithms in IIoT can improve the efficiency of video data utilization and increase the automation and intelligence of the IIoT. Facing the transmission latency problem of massive video data, the algorithms are better deployed in edge devices. However, due to the large size and high computing power requirements of existing object detection algorithms, it is difficult to deploy them on edge devices with limited performance. Therefore, to solve this problem, we propose a smaller and less computationally intensive object detection algorithm, PG-YOLO. We replaced all the convolutional modules of YOLOV5 with low computational ghost modules and streamlined the backbone network to improve the network performance. Then, we propose a better pruning algorithm to compress the network, and finally improve the accuracy by distillation to obtain PG-YOLO. PG-YOLO is smaller and faster and can be deployed in edge devices. In addition, we conducted experimental validation on the SHWD dataset. The experimental results show that PG-YOLO can compress the volume of the model by 9 times compared with YOLOv5s, and the compressed detection accuracy reaches 0.934, with only 0.1% loss of accuracy. Also, the time required for inference is reduced by 32.7%, and the inference speed is improved by 10 FPS. Compared with other object detection algorithms, PG-YOLO also has advantages.INDEX TERMS deep learning, object detection, edge device, lightweight model, PG-YOLO.

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“…F ACIAL motion deblurring for a single image is a specific but critical branches of image deblurring, aimed at restoring a sharp image latent in a motion-blurred face image. Besides being visually unpleasant, blurry face images also degrade the performance of many facial-related computer vision tasks such as face detection [62], [73], [87], face recognition [14], [75], facial emotion recognition [80], [91], and face medical image segmentation [63]. Therefore, face deblurring studies in computer vision and image processing have received much attention.…”

Section: Introductionmentioning

confidence: 99%

Continuous Facial Motion Deblurring

2022

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We introduce a novel framework for continuous facial motion deblurring that restores the continuous sharp moment latent in a single motion-blurred face image via a moment control factor. Although a motion-blurred image is the accumulated signal of continuous sharp moments during the exposure time, most existing single image deblurring approaches aim to restore a fixed number of frames using multiple networks and training stages. To address this problem, we propose a continuous facial motion deblurring network based on GAN (CFMD-GAN), which is a novel framework for restoring the continuous moment latent in a single motion-blurred face image with a single network and a single training stage. To stabilize the network training, we train the generator to restore continuous moments in the order determined by our facial motion-based reordering process (FMR) utilizing domain-specific knowledge of the face. Moreover, we propose an auxiliary regressor that helps our generator produce more accurate images by estimating continuous sharp moments. Furthermore, we introduce a control-adaptive (ContAda) block that performs spatially deformable convolution and channel-wise attention as a function of the control factor. Extensive experiments on the 300VW datasets demonstrate that the proposed framework generates a various number of continuous output frames by varying the moment control factor. Compared with the recent single-tosingle image deblurring networks trained with the same 300VW training set, the proposed method show the superior performance in restoring the central sharp frame in terms of perceptual metrics, including LPIPS, FID and Arcface identity distance. The proposed method outperforms the existing single-to-video deblurring method for both qualitative and quantitative comparisons. In our experiments on the 300VW test set, the proposed framework reached 33.14 dB and 0.93 for recovery of 7 sharp frames in PSNR and SSIM, respectively.

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A robust approach for industrial small-object detection using an improved faster regional convolutional neural network

Cited by 25 publications

References 27 publications

Smart Decision-Making and Communication Strategy in Industrial Internet of Things

Smart Decision-Making and Communication Strategy in Industrial Internet of Things

PG-YOLO: A Novel Lightweight Object Detection Method for Edge Devices in Industrial Internet of Things

Continuous Facial Motion Deblurring

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