CTA-FPN: Channel-Target Attention Feature Pyramid Network for Prohibited Object Detection in X-ray Images

Zhang, Yi; Li, Zhuo; Ma, Chunjie; Li, Jiafeng

doi:10.21203/rs.3.rs-2584406/v1

Cited by 4 publications

(4 citation statements)

References 56 publications

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“…ResNET 101 FPN is the combination of ResNET 101 and Feature Pyramid Network [16]. ResNET 101, has 101 layers consisting of several blocks and skip connections, helping tackle the vanishing gradient problem.…”

Section: F Resnet 101 Fpnmentioning

confidence: 99%

Advancements in Dental Filling Detection Technologies and Strategies for Comprehensive Oral Health Care

Aparna,

Gottumukkala,

Shivampet

et al. 2024

Eng. Technol. Appl. Sci. Res.

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Many individuals face issues with their teeth, requiring the expertise of dentists to provide necessary care. Despite the advancements in dental techniques, there is a persistent shortage of dentists, prompting the development of tools to help the latter efficiently perform patient treatment. The current research focuses on refining the precision of the vital dental treatment known as dental fillings. The approach involves utilizing the Mask Region-based Convolutional Neural Network (MaskRCNN) with different variants of ResNET, such as ResNET50, ResNET101 C4, Dilated C5, and Feature Pyramid Network (FPN), to analyze diverse dental radiographs. By training on a broad range of tooth images, this methodology creates a pixel-based masking system, improving dentists' ability to precisely identify filling levels. Consequently, this innovation contributes significantly to expediting and refining the accuracy of dental treatments, ultimately benefiting individuals with tooth problems. Additionally, as a future prospect, this model can enable robots to perform dental operations as it provides pixel-level information necessary for the treatment.

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Section: F Resnet 101 Fpnmentioning

confidence: 99%

Advancements in Dental Filling Detection Technologies and Strategies for Comprehensive Oral Health Care

Aparna,

Gottumukkala,

Shivampet

et al. 2024

Eng. Technol. Appl. Sci. Res.

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“…They employed CNN to classify the features of drum audio signals across different frequency bands, albeit with relatively low accuracy. Cold et al presented a diagnosis method for conveyor belt transmission drum faults based on minimum entropy deconvolution [6]. By applying optimal filtering to weak fault signals, they extracted features of early-stage faults, albeit with limited fault categories.…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Detection of Belt Conveyor Drum Based on Improved YOLOv8 Network Mode

Du,

Yu,

Wang

et al. 2024

Preprint

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This paper presents a comprehensive study on enhancing the accuracy, real-time performance, and reliability of fault detection in conveyor belt drums. Leveraging insights from two distinct approaches, a novel lightweight network model, YOLOv8n + EMBC + SCC, is proposed. The model integrates the strengths of YOLOv8n in target detection accuracy and speed with innovative modules designed for improved performance. Firstly, the EMBC module, based on DSC high-efficiency convolution, replaces the traditional C2F module in the backbone and neck segments, resulting in a notable 14.5% increase in speed and a 0.7% enhancement in accuracy. Secondly, the SCC efficient convolution module replaces the Conv module in the detection head, further optimizing computational load and model performance, leading to an additional 11.73% increase in speed and a 0.7% improvement in accuracy. Experimental results demonstrate the efficacy of the proposed model, achieving a detection accuracy of 93.4%, surpassing YOLOv8n by 0.9%. Moreover, the model exhibits an improved Frames Per Second (FPS) value of 38.21, representing a 3.56 f/s advancement over YOLOv8n. Heatmap analysis validates the model's superiority in terms of high detection accuracy, precise fault identification, and clear fault localization. This research contributes to the development of a fast, precise, and reliable fault detection system suitable for conveyor belt drum applications, with implications for improving operational efficiency and maintenance practices in industrial settings.

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“…Such algorithms demonstrate elevated detection accuracy but show limitations in real-time performance. The single-stage detection algorithms, on the other hand, discard region selection and directly recognize the target to be detected in the image; representative algorithms include the Single-Shot MultiBox Detector (SSD) [11], the You Only Look Once (YOLO) series [12][13][14], and EfficientDet [15]. In contrast to two-stage detection algorithms, single-stage detection algorithms exhibit superior real-time performance, but the detection accuracy is slightly lower [16].…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Vehicle Detection Method Fusing GSConv and Coordinate Attention Mechanism

Huang,

Tu,

Zhang

et al. 2024

Sensors

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Aiming at the problems of target detection models in traffic scenarios including a large number of parameters, heavy computational burden, and high application cost, this paper introduces an enhanced lightweight real-time detection algorithm, which exhibits higher detection speed and accuracy for vehicle detection. This paper considers the YOLOv7 algorithm as the benchmark model, designs a lightweight backbone network, and uses the MobileNetV3 lightweight network to extract target features. Inspired by the structure of SPPF, the spatial pyramid pooling module is reconfigured by incorporating GSConv, and a lightweight SPPFCSPC-GS module is designed, aiming to minimize the quantity of model parameters and enhance the training speed even further. Furthermore, the CA mechanism is integrated to enhance the feature extraction capability of the model. Finally, the MPDIoU loss function is utilized to optimize the model’s training process. Experiments showcase that the refined YOLOv7 algorithm can achieve 98.2% mAP on the BIT-Vehicle dataset with 52.8% fewer model parameters than the original model and a 35.2% improvement in FPS. The enhanced model adeptly strikes a finer equilibrium between velocity and precision, providing favorable conditions for embedding the model into mobile devices.

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CTA-FPN: Channel-Target Attention Feature Pyramid Network for Prohibited Object Detection in X-ray Images

Cited by 4 publications

References 56 publications

Advancements in Dental Filling Detection Technologies and Strategies for Comprehensive Oral Health Care

Advancements in Dental Filling Detection Technologies and Strategies for Comprehensive Oral Health Care

Research on Fault Detection of Belt Conveyor Drum Based on Improved YOLOv8 Network Mode

A Lightweight Vehicle Detection Method Fusing GSConv and Coordinate Attention Mechanism

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