Underwater object detection algorithm based on attention mechanism and cross-stage partial fast spatial pyramidal pooling

Yan, Jinghui; Zhuang, Zhitao; Zhou, Dujuan; Su, Binghua; Xuanyuan, Zhe; Tang, Jialin; Lai, Yunting; Chen, Jiongjiang; Liang, Wanxin

doi:10.3389/fmars.2022.1056300

Cited by 35 publications

(19 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The selection of the number of integrated CBAM blocks during the incorporation of CBAM modules was done meticulously to minimize any significant increase in the computational overhead on the network. The integration of CBAM modules into the YOLOv7 architecture by researchers to develop novel designs for solving various problems has emerged as a highly popular research area [ 49 , 50 ]. Therefore, the determination of position, number of blocks, and their integration with other network components are design decisions that can vary depending on the specific problem under consideration.…”

Section: Methodsmentioning

confidence: 99%

A novel deep learning-based perspective for tooth numbering and caries detection

Ayhan,

Ayan,

Bayraktar

2024

Clin Oral Invest

View full text Add to dashboard Cite

Objectives The aim of this study was automatically detecting and numbering teeth in digital bitewing radiographs obtained from patients, and evaluating the diagnostic efficiency of decayed teeth in real time, using deep learning algorithms. Methods The dataset consisted of 1170 anonymized digital bitewing radiographs randomly obtained from faculty archives. After image evaluation and labeling process, the dataset was split into training and test datasets. This study proposed an end-to-end pipeline architecture consisting of three stages for matching tooth numbers and caries lesions to enhance treatment outcomes and prevent potential issues. Initially, a pre-trained convolutional neural network (CNN) utilized to determine the side of the bitewing images. Then, an improved CNN model YOLOv7 was proposed for tooth numbering and caries detection. In the final stage, our developed algorithm assessed which teeth have caries by comparing the numbered teeth with the detected caries, using the intersection over union value for the matching process. Results According to test results, the recall, precision, and F1-score values were 0.994, 0.987 and 0.99 for teeth detection, 0.974, 0.985 and 0.979 for teeth numbering, and 0.833, 0.866 and 0.822 for caries detection, respectively. For teeth numbering and caries detection matching performance; the accuracy, recall, specificity, precision and F1—Score values were 0.934, 0.834, 0.961, 0.851 and 0.842, respectively. Conclusions The proposed model exhibited good achievement, highlighting the potential use of CNNs for tooth detection, numbering, and caries detection, concurrently. Clinical significance CNNs can provide valuable support to clinicians by automating the detection and numbering of teeth, as well as the detection of caries on bitewing radiographs. By enhancing overall performance, these algorithms have the capacity to efficiently save time and play a significant role in the assessment process.

show abstract

Section: Methodsmentioning

confidence: 99%

A novel deep learning-based perspective for tooth numbering and caries detection

Ayhan,

Ayan,

Bayraktar

2024

Clin Oral Invest

View full text Add to dashboard Cite

show abstract

“…That is, the running speed is further improved by fusing the feature maps of various receptive fields, which enriches the expressive power of the feature maps. With the continuous innovation of the YOLO algorithm, the module for the spatial pyramid pooling is further improved into SPPCSPC 27 and SPPFCSPC 28 . The CSP structure is utilized to divide the features into two parts, one of which is processed for regular convolution, and the other is processed for the SPP structure.…”

Section: Methodsmentioning

confidence: 99%

Surface defect detection of industrial components based on vision

Chen,

Feng,

Liu

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Early and effective surface defect detection in industrial components can avoid the occurrence of serious safety hazards. Since most industrial component surfaces have tiny defects with high similarity to the detection background, there are often issues of missed or false detections when defects are detected, leading to low detection accuracy. To deal with the aforementioned issue, this essay suggests a high-precision detection model for surface defects in industrial components based on the YOLOv5 algorithm. First, the original spatial pyramid pooling (SPPF) is innovated by proposing the SPPFKCSPC module, which improves the network's capacity for feature extraction from targets at different scales and fuses multiscale features better. Then, C3 is combined with SPPFKCSPC and replaces the C3 module of the backbone network, which improves feature expression and enhances the receptive field of the network. Finally, the coordinate attention mechanism (CA) has been embedded into the YOLOv5 neck network, and the bounding box regression loss function of the algorithm is improved to EIOU, not only improving the precision of the target localization and recognition model but also enhancing the overall network performance. Based on the public datasets NEU-DET and PV-Multi-Defect, multiple sets of experiments were conducted using innovative algorithms. On the NEU-DET dataset, we got a mean average accuracy (mAP) of 88.3%, which is 7.2% greater than the original approach. On the PV-Multi-Defect dataset, the mAP value reached 97.5%, an improvement of 1.5%. As shown by the experimental data, the detection results significantly improved.

show abstract

“…A detailed explanation of CBAM, CAM, and SAM can be found in [26]. Recent studies, including [27,28], have demonstrated successful combinations of convolutional block attention modules (CBAM) with convolutional neural networks in computer vision tasks. However, these studies have not provided the source code for the implementation of CBAM modules or the configuration of the network structure, making the reproducibility of reported results unfeasible.…”

Section: Convolutional Block Attention Module (Cbam)mentioning

confidence: 99%

Yolo-Papaya: A Papaya Fruit Disease Detector and Classifier Using CNNs and Convolutional Block Attention Modules

et al. 2023

View full text Add to dashboard Cite

Agricultural losses due to post-harvest diseases can reach up to 30% of total production. Detecting diseases in fruits at an early stage is crucial to mitigate losses and ensure the quality and health of fruits. However, this task is challenging due to the different formats, sizes, shapes, and colors that the same disease can present. Convolutional neural networks have been proposed to address this issue, but most studies use self-built datasets with few samples per disease, hindering reproducibility and comparison of techniques. To address these challenges, the authors proposed a novel image dataset comprising 23,158 examples divided into nine classes of papaya fruit diseases, and a robust papaya fruit disease detector called Yolo-Papaya based on the YoloV7 detector with the implementation of a convolutional block attention module (CBAM) attention mechanism. This detector achieved an overall mAP (mean average precision) of 86.2%, with a performance of over 98% in classes such as “healthy fruits” and “Phytophthora blight”. The proposed detector and dataset can be used in practical applications for fruit quality control and are consolidated as a robust benchmark for the task of papaya fruit disease detection. The image dataset and all source code used in this study are available to the academic community on the project page, enabling reproducibility of the study and advancement of research in this domain.

show abstract

Underwater object detection algorithm based on attention mechanism and cross-stage partial fast spatial pyramidal pooling

Cited by 35 publications

References 29 publications

A novel deep learning-based perspective for tooth numbering and caries detection

A novel deep learning-based perspective for tooth numbering and caries detection

Surface defect detection of industrial components based on vision

Yolo-Papaya: A Papaya Fruit Disease Detector and Classifier Using CNNs and Convolutional Block Attention Modules

Contact Info

Product

Resources

About