Detecting Motion Blurred Vehicle Logo in IoV Using Filter-DeblurGAN and VL-YOLO

Zhou, Linghua; Min, Weidong; Lin, Deyu; Han, Qing; Liu, Ruikang

doi:10.1109/tvt.2020.2969427

Cited by 55 publications

(12 citation statements)

References 46 publications

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“…Action recognition is difficult to achieve due to large intraclass otherness, nondeterminacy of different actions, and difficult-to-annotate large-scale datasets. Many researchers have focused on action recognition using convolution networks [ 21 – 24 ] and applications [ 7 – 9 ]. Action recognition and object detection have similar notions in technology.…”

Section: Related Workmentioning

confidence: 99%

Action Recognition Using Action Sequences Optimization and Two-Stream 3D Dilated Neural Network

Xiong

Min

Han

et al. 2022

Computational Intelligence and Neuroscience

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Effective extraction and representation of action information are critical in action recognition. The majority of existing methods fail to recognize actions accurately because of interference of background changes when the proportion of high-activity action areas is not reinforced and by using RGB flow alone or combined with optical flow. A novel recognition method using action sequences optimization and two-stream fusion network with different modalities is proposed to solve these problems. The method is based on shot segmentation and dynamic weighted sampling, and it reconstructs the video by reinforcing the proportion of high-activity action areas, eliminating redundant intervals, and extracting long-range temporal information. A two-stream 3D dilated neural network that integrates features of RGB and human skeleton information is also proposed. The human skeleton information strengthens the deep representation of humans for robust processing, alleviating the interference of background changes, and the dilated CNN enlarges the receptive field of feature extraction. Compared with existing approaches, the proposed method achieves superior or comparable classification accuracies on benchmark datasets UCF101 and HMDB51.

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Section: Related Workmentioning

confidence: 99%

Action Recognition Using Action Sequences Optimization and Two-Stream 3D Dilated Neural Network

Xiong

Min

Han

et al. 2022

Computational Intelligence and Neuroscience

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“…Xie L, Ahmad T, Jin L, Liu Y [9] proposed MD-YOLO that is able to predict the tilt angle of license plates by improving the output dimension of YOLO. VL-YOLO [10] was proposed by improving the framework of YOLOv3, it is more suitable for the detection of small-sized object compared to YOLOv3. IN-YOLO [11] is used to monitor surface condition of outdoor high voltage insulation.…”

Section: A Object Detection Algorithmsmentioning

confidence: 99%

A Surveillance Video Real-Time Analysis System Based on Edge-Cloud and FL-YOLO Cooperation in Coal Mine

Zhang

2021

IEEE Access

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Video monitoring is an important means to ensure production safety in coal mine. However, the currently intelligent video surveillance is difficult to respond in real-time due to the latency of cloud computing. In this paper, a cloud-edge cooperation framework is proposed, which integrates cloud computing and edge computing in a coordinated manner. The cloud computing is used to process non-realtime and global tasks, while the edge computing is responsible for handling local monitoring video in realtime. In order to realize cloud-edge data interaction and online optimization for edge models, the heterogeneous converged network is built. In addition, an object detection model FL-YOLO composed of depthwise separable convolution and down-sampling inverted residual block is proposed, which realizes real-time video analysis at the edge. Finally, this paper discusses the complexity of FL-YOLO by its computational cost and model size. The experiment results show that the model size of FL-YOLO is 16.1MB, which is very light, and it achieves 36.7 FPS on NVIDIA Jetson TX1 and an AP of 76.7% on Multi-scene pedestrian dataset. Comparing with mainstream object detection models, FL-YOLO completes faster detection speed and higher accuracy, and it has lower calculation complexity and smaller model scale. Furthermore, the AP on Single-scene pedestrian dataset of FL-YOLO is improved to 80.7% by cloud-edge cooperation. K-Fold method is also used to further compared the performance of FL-YOLO and other models. Moreover, system test is implemented on coal mine, which validates the actual engineering effect of the proposed cloud-edge cooperation framework.INDEX TERMS Edge computing, YOLO, cloud-edge cooperation, real-time analysis.

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“…In addition, we measured the processing speed of marker detection by our method on the actual embedded system for the processing on the drone and compared them with the state-of-the-art methods. The research [ 19 ] studied the detection of motion-blurred vehicle logo. However, its target was only for logo detection, which was different from our research of marker detection by a drone camera.…”

Section: Related Workmentioning

confidence: 99%

“…You only look once (YOLO) models might be the most popular deep object detectors in practical applications, because the detection accuracy and execution time are well balanced. Nevertheless, those systems have low robustness and are prone to failure when dealing with low-resolution [ 16 ] or motion-blurred images [ 19 ]. Such inputs need to be preprocessed before being fed to the detector.…”

Section: Introductionmentioning

confidence: 99%

SlimDeblurGAN-Based Motion Deblurring and Marker Detection for Autonomous Drone Landing

Truong

Lee

Owais

et al. 2020

Sensors

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Deep learning-based marker detection for autonomous drone landing is widely studied, due to its superior detection performance. However, no study was reported to address non-uniform motion-blurred input images, and most of the previous handcrafted and deep learning-based methods failed to operate with these challenging inputs. To solve this problem, we propose a deep learning-based marker detection method for autonomous drone landing, by (1) introducing a two-phase framework of deblurring and object detection, by adopting a slimmed version of deblur generative adversarial network (DeblurGAN) model and a You only look once version 2 (YOLOv2) detector, respectively, and (2) considering the balance between the processing time and accuracy of the system. To this end, we propose a channel-pruning framework for slimming the DeblurGAN model called SlimDeblurGAN, without significant accuracy degradation. The experimental results on the two datasets showed that our proposed method exhibited higher performance and greater robustness than the previous methods, in both deburring and marker detection.

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Detecting Motion Blurred Vehicle Logo in IoV Using Filter-DeblurGAN and VL-YOLO

Cited by 55 publications

References 46 publications

Action Recognition Using Action Sequences Optimization and Two-Stream 3D Dilated Neural Network

Action Recognition Using Action Sequences Optimization and Two-Stream 3D Dilated Neural Network

A Surveillance Video Real-Time Analysis System Based on Edge-Cloud and FL-YOLO Cooperation in Coal Mine

SlimDeblurGAN-Based Motion Deblurring and Marker Detection for Autonomous Drone Landing

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