Smoking Behavior Detection Based On Improved YOLOv5s Algorithm

Tang, Junlong; Liu, Shenbo; Zheng, Bei; Zhang, Jun; Wang, Bin; Yang, Mukun

doi:10.1109/isne48910.2021.9493637

Cited by 14 publications

(3 citation statements)

References 1 publication

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“…The Neck part primarily serves the purpose of feature extraction. The process of up-sampling and down-sampling is achieved through the use of an FPN (Feature Pyramid Net) and PAN (Path Aggregation Network) [25]. As shown in Figure 1, three sizes of feature maps, namely 2020, 4040, and 80 × 80, are obtained to facilitate the fusion of multiscale features.…”

Section: Yolov5 Algorithmmentioning

confidence: 99%

Application of Improved YOLOv5 Algorithm in Lightweight Transmission Line Small Target Defect Detection

Yu,

Lei,

Shen

et al. 2024

Electronics

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With the development of UAV automatic cruising along power transmission lines, intelligent defect detection in aerial images has become increasingly important. In the process of target detection for aerial photography of transmission lines, insulator defects often pose challenges due to complex backgrounds, resulting in noisy images and issues such as slow detection speed, leakage, and the misidentification of small-sized targets. To address these challenges, this paper proposes an insulator defect detection algorithm called DFCG_YOLOv5, which focuses on improving both the accuracy and speed by enhancing the network structure and optimizing the loss function. Firstly, the input part is optimized, and a High-Speed Adaptive Median Filtering (HSMF) algorithm is introduced to preprocess the images captured by the UAV system, effectively reducing the noise interference in target detection. Secondly, the original Ghost backbone structure is further optimized, and the DFC attention mechanism is incorporated to strike a balance between the target detection accuracy and speed. Additionally, the original CIOU loss function is replaced with the Poly Loss, which addresses the issue of imbalanced positive and negative samples for small targets. By adjusting the parameters for different datasets, this modification effectively suppresses background positive samples and enhances the detection accuracy. To align with real-world engineering applications, the dataset utilized in this study consists of unmanned aircraft system machine patrol images from the Yunnan Power Supply Bureau Company. The experimental results demonstrate a 9.2% improvement in the algorithm accuracy and a 26.2% increase in the inference speed compared to YOLOv5s. These findings hold significant implications for the practical implementation of target detection in engineering scenarios.

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Section: Yolov5 Algorithmmentioning

confidence: 99%

Application of Improved YOLOv5 Algorithm in Lightweight Transmission Line Small Target Defect Detection

Yu,

Lei,

Shen

et al. 2024

Electronics

View full text Add to dashboard Cite

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“…To increase information flow, YOLOv5 adopts PANet in the neck. PANet uses a feature pyramid structure (FPN) to fuse low-and high-level feature maps [42]. Localisation and robust semantic features are retained in the network with the use of FPN structures.…”

Section: Yolomentioning

confidence: 99%

Real-Time Embedded Implementation of Improved Object Detector for Resource-Constrained Devices

Ravi

El-Sharkawy

2022

JLPEA

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Artificial intelligence (A.I.) has revolutionised a wide range of human activities, including the accelerated development of autonomous vehicles. Self-navigating delivery robots are recent trends in A.I. applications such as multitarget object detection, image classification, and segmentation to tackle sociotechnical challenges, including the development of autonomous driving vehicles, surveillance systems, intelligent transportation, and smart traffic monitoring systems. In recent years, object detection and its deployment on embedded edge devices have seen a rise in interest compared to other perception tasks. Embedded edge devices have limited computing power, which impedes the deployment of efficient detection algorithms in resource-constrained environments. To improve on-board computational latency, edge devices often sacrifice performance, creating the need for highly efficient A.I. models. This research examines existing loss metrics and their weaknesses, and proposes an improved loss metric that can address the bounding box regression problem. Enhanced metrics were implemented in an ultraefficient YOLOv5 network and tested on the targeted datasets. The latest version of the PyTorch framework was incorporated in model development. The model was further deployed using the ROS 2 framework running on NVIDIA Jetson Xavier NX, an embedded development platform, to conduct the experiment in real time.

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“…Some scholars [3] [4] started from smoke characteristics and used the unique color characteristics of smoking smoke to achieve the localization of suspected smoke areas. Wan Li-Bo [5] and others considered the a priori conditions of human faces. Some scholars [2][7] [8] used cigarette recognition as an entry point to achieve smoking detection.…”

Section: Introductionmentioning

confidence: 99%

Substation smoking behavior detection based on improved decoupling head

Zhang

Liang

Mei³

et al. 2023

Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023)

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In the complex environment of the substation, small objects such as long-distance cigarettes, cigarette boxes, and lighters have few imaging pixels and lack texture information, making it difficult for convolutional neural networks to extract small object features. In the case of multiple targets, the missed detection rate and false detection rate of small targets are high, and the fusion of model features is insufficient, making it difficult to accurately identify and detect. Aiming at the above problems, a multi-scale small target detection algorithm is proposed. In the prediction part of the network, a more effective decoupling head is designed. In addition, shallow features are introduced to improve the feature pyramid, extract small target features, increase the correlation between multiple targets, and prevent the loss of small target feature information. At the same time, a multi-layer attention mechanism is embedded in the backbone network to increase the regional features of invisible small targets and reduce the missed detection rate. In the post-processing stage, the Focal Loss loss function is introduced to increase the model's learning of positive sample targets and further reduce the rate of missed detection and false detection. The experimental results show that the method achieves a 𝑚𝐴𝑃@. 5: .95 of 0.6350 on the homemade smoking dataset, and 𝑚𝐴𝑃@0.5 achieves 0.9569. For the self-made multi-scene and multi-scale smoking data set, this model has advantages in detection accuracy compared with the current excellent target detection models such as YOLOX and YOLOv5. The experimental results show that the model method can realize the identification and detection of small targets such as cigarettes and lighters under multi-scale targets, which has a certain reference value for anti-smoking measures.

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Smoking Behavior Detection Based On Improved YOLOv5s Algorithm

Cited by 14 publications

References 1 publication

Application of Improved YOLOv5 Algorithm in Lightweight Transmission Line Small Target Defect Detection

Application of Improved YOLOv5 Algorithm in Lightweight Transmission Line Small Target Defect Detection

Real-Time Embedded Implementation of Improved Object Detector for Resource-Constrained Devices

Substation smoking behavior detection based on improved decoupling head

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