Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors

Guirguis, Karim; Abdelsamad, Mohamed; Eskandar, George; Hendawy, Ahmed; Matthias, Kayser,; Yang, Bin; Beyerer, Jürgen

doi:10.1109/wacv56688.2023.00375

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…Li et al [17] constructed an MTC-YOLOV5n model (a one-stage detection model) to detect three types of pumpkin diseases under complex backgrounds, with an average detection accuracy of 84.9% and an FPS of 143. Many experiments have shown that the one-stage detection model has slightly lower detection accuracy, but its detection speed is faster than that of the two-stage detection model [18][19][20][21][22][23]. Therefore, the one-stage detection model has more advantages for plant disease detection applied in actual agricultural production and mobile terminals.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Algorithm for Recognizing Pear Leaf Diseases in Natural Scenes Based on an Improved YOLOv5 Deep Learning Model

Li,

Liu,

Wang

2024

Agriculture

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The precise detection of diseases is crucial for the effective treatment of pear trees and to improve their fruit yield and quality. Currently, recognizing plant diseases in complex backgrounds remains a significant challenge. Therefore, a lightweight CCG-YOLOv5n model was designed to efficiently recognize pear leaf diseases in complex backgrounds. The CCG-YOLOv5n model integrates a CA attention mechanism, CARAFE up-sampling operator, and GSConv into YOLOv5n. It was trained and validated using a self-constructed dataset of pear leaf diseases. The model size and FLOPs are only 3.49 M and 3.8 G, respectively. The mAP@0.5 is 92.4%, and the FPS is up to 129. Compared to other lightweight models, the experimental results demonstrate that the CCG-YOLOv5n achieves higher average detection accuracy and faster detection speed with a smaller computation and model size. In addition, the robustness comparison test indicates that the CCG-YOLOv5n model has strong robustness under various lighting and weather conditions, including frontlight, backlight, sidelight, tree shade, and rain. This study proposed a CCG-YOLOv5n model for accurately detecting pear leaf diseases in complex backgrounds. The model is suitable for use on mobile terminals or devices.

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Section: Introductionmentioning

confidence: 99%

A Lightweight Algorithm for Recognizing Pear Leaf Diseases in Natural Scenes Based on an Improved YOLOv5 Deep Learning Model

Li,

Liu,

Wang

2024

Agriculture

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FSPDD: A double-branch attention guided network for few-shot PCB defect detection

Shi,

Xu,

Cao

et al. 2024

Multimed Tools Appl

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During the production of printed circuit board (PCB), there will be defects due to inappropriate operations, which will affect the use of electronic products. Majority defect detection methods cost a large number of annotated samples to train detection models. However, PCB defect samples are difficult to collect. Moreover, existing few-shot object detection methods tend to extracting low-level features from support and query images via the shared backbone such as ResNet-50. However, it is not sufficient to obtain fine-grained prior guidance. To address the above issues, we propose a few-shot PCB defect detection model with double-branch attention. Specifically, the joint attention enhancement (JAE) module is proposed to fully mine effective information of query PCB images in multiple dimensions to enhance the representation of latent defects. Then, the multi-scale guidance (MSG) module is proposed to integrate prior knowledge within support PCB images into vectors to reweight query PCB images. Experiments on the PCB defect dataset demonstrate that AP of FSPDD outperforms state-of-the-art methods under different shot settings (k=1,2,3,5,10,30) and our proposed FSPDD has a good generalization ability, in which AP reachs 0.273 when $$k=30$$ k = 30 and is 5.28% higher than SOTA methods.

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Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors

Cited by 2 publications

References 28 publications

A Lightweight Algorithm for Recognizing Pear Leaf Diseases in Natural Scenes Based on an Improved YOLOv5 Deep Learning Model

A Lightweight Algorithm for Recognizing Pear Leaf Diseases in Natural Scenes Based on an Improved YOLOv5 Deep Learning Model

FSPDD: A double-branch attention guided network for few-shot PCB defect detection

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