Real-Time Target Detection in Visual Sensing Environments Using Deep Transfer Learning and Improved Anchor Box Generation

Ren, Zhaoyu; Lam, Edmund Y.; Zhao, Jianlin

doi:10.1109/access.2020.3032955

Cited by 8 publications

(2 citation statements)

References 37 publications

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“…This scheme is feasible for fast detection but cannot capture the encoded information of immense small objects present in high dimensional maps. In [26], the authors suggested a transfer learning-based solution to increase the detection speed of the network. They used a K++ means clustering algorithm to revise the anchor boxes.…”

Section: Generalized Deep Network In Smoke Detectionmentioning

confidence: 99%

Early Smoke Detection Based on Improved YOLO-PCA Network

Zhang

Dai

et al. 2022

Fire

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Early detection of smoke having indistinguishable pixel intensities in digital images is a difficult task. To better maintain fire surveillance, early smoke detection is crucial. To solve the problem, we have integrated the principal component analysis (PCA) as a pre-processing module with the improved version of You Only Look Once (YOLOv3). The ordinary YOLOv3 structure has been improved after inserting one extra detection scale at stride-4 specifically to detect immense small smoke instances in the wild. The improved network design establishes a sequential relation between feature maps of lower spatial information and fine-grained semantic information in up-sampled maps via skip connections and concatenation operations. The testing of the improved model is carried out on self-prepared smoke datasets. In digital images, the smoke instances are captured in various complicated environments, for example, the mountains and fog in the background. A principal component analysis (PCA) helps in useful features selection and abandons the involvement of redundant features in the testing of the trained network hence, overcoming the latency at inference stage. In addition, to process small smoke images as positive samples during training, new sizes of anchors are calculated on small smoke data at a specified Intersection over Union (IoU) threshold. The experimental results show the improvement in precision rate, recall rate, and mean harmonic (F1-score) by 2.67, 3.06, and 5.59 percentages. The respective improvements in average precision (AP) and mean average precision (mAP) are 1.66 and 2.78 percentages.

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Section: Generalized Deep Network In Smoke Detectionmentioning

confidence: 99%

Early Smoke Detection Based on Improved YOLO-PCA Network

Zhang

Dai

et al. 2022

Fire

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“…Nevertheless, the abundance of surveillance images demands increased labor costs, a process that consumes time and fails to ensure the precision of pedestrian identification. The integration of intelligent target detection models rooted in deep learning into smart city contexts holds promise for enhancing urban security systems [3], thereby reducing the property damage and casualties caused by criminals with a lower cost and higher efficiency. This model can be applied to 24 h pedestrian target detection in smart cities thanks to the creation of a comprehensive dataset that enhances its adaptability to various scenarios.…”

Section: Introductionmentioning

confidence: 99%

YOLOv5-MS: Real-Time Multi-Surveillance Pedestrian Target Detection Model for Smart Cities

Song,

2023

Biomimetics

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Intelligent video surveillance plays a pivotal role in enhancing the infrastructure of smart urban environments. The seamless integration of multi-angled cameras, functioning as perceptive sensors, significantly enhances pedestrian detection and augments security measures in smart cities. Nevertheless, current pedestrian-focused target detection encounters challenges such as slow detection speeds and increased costs. To address these challenges, we introduce the YOLOv5-MS model, an YOLOv5-based solution for target detection. Initially, we optimize the multi-threaded acquisition of video streams within YOLOv5 to ensure image stability and real-time performance. Subsequently, leveraging reparameterization, we replace the original BackBone convolution with RepvggBlock, streamlining the model by reducing convolutional layer channels, thereby enhancing the inference speed. Additionally, the incorporation of a bioinspired “squeeze and excitation” module in the convolutional neural network significantly enhances the detection accuracy. This module improves target focusing and diminishes the influence of irrelevant elements. Furthermore, the integration of the K-means algorithm and bioinspired Retinex image augmentation during training effectively enhances the model’s detection efficacy. Finally, loss computation adopts the Focal-EIOU approach. The empirical findings from our internally developed smart city dataset unveil YOLOv5-MS’s impressive 96.5% mAP value, indicating a significant 2.0% advancement over YOLOv5s. Moreover, the average inference speed demonstrates a notable 21.3% increase. These data decisively substantiate the model’s superiority, showcasing its capacity to effectively perform pedestrian detection within an Intranet of over 50 video surveillance cameras, in harmony with our stringent requisites.

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Advanced Optical Imaging Technologies for Microplastics Identification: Progress and Challenges

Zhu,

Li,

Huang

et al. 2024

Advanced Photonics Research

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Global concern about microplastic (MP) and nanoplastic (NP) particles is continuously rising with their proliferation worldwide. Effective identification methods for MP and NP pollution monitoring are highly needed, but due to different requirements and technical challenges, much of the work is still in progress. Herein, the advanced optical imaging systems that are successfully applied or have the potential for MP identification are focused on. Compared with chemical and thermal analyses, optical methods have the unique advantages of being nondestructive and noncontact and allow fast detection without complex sample preprocessing. Furthermore, they are capable of revealing the morphology, anisotropy, and material characteristics of MP for their quick and robust detection. This review aims to present a comprehensive discussion of the relevant optical imaging systems, emphasizing their operating principles, strengths, and drawbacks. Multiple comparisons and analyses among these technologies are conducted in order to provide practical guidelines for researchers. In addition, the combination of optical and other alternative technologies is described and the representative portable MP detection devices are highlighted. Together, they shed light on the prospects for long‐term MP pollution monitoring and environmental protection.

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Real-Time Target Detection in Visual Sensing Environments Using Deep Transfer Learning and Improved Anchor Box Generation

Cited by 8 publications

References 37 publications

Early Smoke Detection Based on Improved YOLO-PCA Network

Early Smoke Detection Based on Improved YOLO-PCA Network

YOLOv5-MS: Real-Time Multi-Surveillance Pedestrian Target Detection Model for Smart Cities

Advanced Optical Imaging Technologies for Microplastics Identification: Progress and Challenges

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