Crowd Detection Algorithm for Complex Scene Based on Improved Yolov3

li, zhihong; Wang, X.; Wen, Yanjie; Zhao, Li; Gao, Xiuli

doi:10.2139/ssrn.4142433

Cited by 5 publications

(8 citation statements)

References 5 publications

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“…Additionally, the UAV20L dataset includes large changes in shooting perspectives and challenging factors such as occlusions, which place stricter requirements on the robustness of tracking algorithms in long video tasks. The experimental results show that our algorithm outperforms most of the state‐of‐the‐art algorithms in terms of success rate and precision for most of the challenges in the dataset [56].…”

Section: Discussionmentioning

confidence: 99%

Robust object tracking via ensembling semantic‐aware network and redetection

Liu

Liang

et al. 2023

IET Computer Vision

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Most Siamese‐based trackers use classification and regression to determine the target bounding box, which can be formulated as a linear matching process of the template and search region. However, this only takes into account the similarity of features while ignoring the semantic object information, resulting in some cases in which the regression box with the highest classification score is not accurate. To address the lack of semantic information, an object tracking approach based on an ensemble semantic‐aware network and redetection (ESART) is proposed. Furthermore, a DarkNet53 network with transfer learning is used as our semantic‐aware model to adapt the detection task for extracting semantic information. In addition, a semantic tag redetection method to re‐evaluate the bounding box and overcome inaccurate scaling issues is proposed. Extensive experiments based on OTB2015, UAV123, UAV20L, and GOT‐10k show that our tracker is superior to other state‐of‐the‐art trackers. It is noteworthy that our semantic‐aware ensemble method can be embedded into any tracker for classification and regression task.

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Section: Discussionmentioning

confidence: 99%

Robust object tracking via ensembling semantic‐aware network and redetection

Liu

Liang

et al. 2023

IET Computer Vision

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“…To address this problem, Li et al. [37] employed a weighted K‐means clustering algorithm to optimize the anchors. Weng et al.…”

Section: Methodsmentioning

confidence: 99%

“…To address this problem, Li et al [37] employed a weighted K-means clustering algorithm to optimize the anchors. Weng et al [38] used a K-means II clustering algorithm to improve the region suggestion.…”

Section: Basic Ideamentioning

confidence: 99%

ACD‐YOLO: Improved YOLOv5‐based method for steel surface defects detection

Fan,

Wang,

et al. 2023

IET Image Processing

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Since the quality of steel is of paramount importance in modern production, the defects detection of steel surface is significantly crucial. In this field, two‐stage detection algorithms have encountered issues about low detection speed, while one‐stage detection algorithms have room for improvement in detection accuracy. How to trade‐off between accuracy and speed of detection to better meet the demands of industrial production remains a challenge. To address this problem, this paper proposes a You Only Look Once version 5 (YOLOv5)‐based improved method ACD‐YOLO. ACD‐YOLO model incorporates anchors optimization, context augmentation module, and efficient convolution operators. In anchor optimization, the boundaries of anchors are optimized using an improved genetic algorithm. Moreover, to improve detection accuracy, a context augmentation module is incorporated into both the head and the backbone end of the network. Additionally, efficient convolution operators are adopted to address the increase of computation complexity caused by the context augmentation module. Experimental results show that ACD‐YOLO achieves mean average precision of 79.3%, with frames per second of 72. Compared to reference methods, ACD‐YOLO achieves the best balance between accuracy and speed of detection, and is more suitable for practice industrial production.

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“…Weng et al [28] introduced an enhanced Mask R-CNN technique that uses the k-means II clustering algorithm to improve the production of anchor boxes, eliminates the mask branch, and achieves a detection rate of 5.9 frames per second. On the basis of YOLOv3, Li et al [29] adjusted the a priori frame settings using the weighted K-means method and added a large-scale detection layer, resulting in an 11% increase in detection accuracy. Based on the Resnet50 and MobilenetV3 models, Yuan et al [30] created an enhanced lightweight neural network model using the knowledge distillation technique.…”

Section: The Deep-learning-based Methodsmentioning

confidence: 99%

Surface Defect Detection of Rolled Steel Based on Lightweight Model

Zhou

Yun

et al. 2022

Applied Sciences

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A lightweight rolled steel strip surface defect detection model, YOLOv5s-GCE, is proposed to improve the efficiency and accuracy of industrialized rolled steel strip defect detection. The Ghost module is used to replace the CBS structure in a part of the original YOLOv5s model, and the Ghost bottleneck is employed to replace the bottleneck structure in C3 to minimize the model’s size and make the network lightweight. The EIoU function is added to improve the accuracy of the regression of the prediction frame and accelerate its convergence. The CA (Coordinate Attention) attention method is implemented to reinforce critical feature channels and their position information, enabling the model to identify and find targets correctly. The experimental results demonstrate that the accuracy of YOLOv5s-GCE is 85.7%, which is 3.5% higher than that of the original network; the model size is 7.6 MB, which is 44.9% smaller than that of the original network; the number of model parameters and calculations are reduced by 47.1% and 48.8%, respectively; and the detection speed reached 58.8 fps. YOLOv5s-GCE meets the necessity for real-time identification of rolled steel flaws in industrial production compared to other common algorithms.

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Crowd Detection Algorithm for Complex Scene Based on Improved Yolov3

Cited by 5 publications

References 5 publications

Robust object tracking via ensembling semantic‐aware network and redetection

Robust object tracking via ensembling semantic‐aware network and redetection

ACD‐YOLO: Improved YOLOv5‐based method for steel surface defects detection

Surface Defect Detection of Rolled Steel Based on Lightweight Model

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