A Lightweight One-Stage Defect Detection Network for Small Object Based on Dual Attention Mechanism and PAFPN

Zhang, Yue; Xie, Fei; Huang, Lei; Shi, Jianjun; Yang, Jiale; Li, Zongan

doi:10.3389/fphy.2021.708097

Cited by 29 publications

(21 citation statements)

References 18 publications

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“…Model Neck and Head : The model of YOLOv5x-hens added bottom-up path augmentation by using PAFPN [ 31 ], which is a feature pyramid module. The neck utilizes different feature pyramids to recognize the same chicken under diverse sizes and scales.…”

Section: Methodsmentioning

confidence: 99%

A Deep Learning Model for Detecting Cage-Free Hens on the Litter Floor

Yang

Chai

Bist

et al. 2022

Animals

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Real-time and automatic detection of chickens (e.g., laying hens and broilers) is the cornerstone of precision poultry farming based on image recognition. However, such identification becomes more challenging under cage-free conditions comparing to caged hens. In this study, we developed a deep learning model (YOLOv5x-hens) based on YOLOv5, an advanced convolutional neural network (CNN), to monitor hens’ behaviors in cage-free facilities. More than 1000 images were used to train the model and an additional 200 images were adopted to test it. One-way ANOVA and Tukey HSD analyses were conducted using JMP software (JMP Pro 16 for Mac, SAS Institute, Cary, North Caronia) to determine whether there are significant differences between the predicted number of hens and the actual number of hens under various situations (i.e., age, light intensity, and observational angles). The difference was considered significant at p < 0.05. Our results show that the evaluation metrics (Precision, Recall, F1 and mAP@0.5) of the YOLOv5x-hens model were 0.96, 0.96, 0.96 and 0.95, respectively, in detecting hens on the litter floor. The newly developed YOLOv5x-hens was tested with stable performances in detecting birds under different lighting intensities, angles, and ages over 8 weeks (i.e., birds were 8–16 weeks old). For instance, the model was tested with 95% accuracy after the birds were 8 weeks old. However, younger chicks such as one-week old birds were harder to be tracked (e.g., only 25% accuracy) due to interferences of equipment such as feeders, drink lines, and perches. According to further data analysis, the model performed efficiently in real-time detection with an overall accuracy more than 95%, which is the key step for the tracking of individual birds for evaluation of production and welfare. However, there are some limitations of the current version of the model. Error detections came from highly overlapped stock, uneven light intensity, and images occluded by equipment (i.e., drinking line and feeder). Future research is needed to address those issues for a higher detection. The current study established a novel CNN deep learning model in research cage-free facilities for the detection of hens, which provides a technical basis for developing a machine vision system for tracking individual birds for evaluation of the animals’ behaviors and welfare status in commercial cage-free houses.

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Section: Methodsmentioning

confidence: 99%

A Deep Learning Model for Detecting Cage-Free Hens on the Litter Floor

Yang

Chai

Bist

et al. 2022

Animals

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“…This model proposed in [110] achieved a detection mAP of 44.3%, which was much higher than that of the traditional FCOS model. The detector achieves proposal free and anchor free, significantly reducing the number of parameters.…”

Section: A Cnn-based Methods For Surface Defectsmentioning

confidence: 82%

“…Zhang et al [110] modified the Fully Convolutional One-Stage (FCOS) [111] network to achieve surface defect detection in PCBs. The authors replaced the backbone network of ResNet101 [97] with a light network of MobileNetV2 [112] to decrease model parameters.…”

Section: A Cnn-based Methods For Surface Defectsmentioning

confidence: 99%

Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey

Qin

Isa

2023

IEEE Access

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“…Object detection is an essential part of computer vision tasks, which supports many downstream tasks [35][36][37][38]. Methods based on convolutional neural networks can be summarized into two categories: single-stage [39][40][41][42][43] and multi-stage [44][45][46]. In recent years, compared with multi-stage detection methods, single-stage detection methods have been widely adopted due to their simple design and powerful performance.…”

Section: Generic Object Detectionmentioning

confidence: 99%

Atomic number prior guided network for prohibited items detection from heavily cluttered X-ray imagery

et al. 2023

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Prohibited item detection in X-ray images is an effective measure to maintain public safety. Recent prohibited item detection methods based on deep learning has achieved impressive performance. Some methods improve prohibited item detection performance by introducing prior knowledge of prohibited items, such as the edge and size of an object. However, items within baggage are often placed randomly, resulting in cluttered X-ray images, which can seriously affect the correctness and effectiveness of prior knowledge. In particular, we find that different material items in X-ray images have clear distinctions according to their atomic number Z information, which is vital to suppress the interference of irrelevant background information by mining material cues. Inspired by this observation, in this paper, we combined the atomic number Z feature and proposed a novel atomic number Z Prior Guided Network (ZPGNet) to detect prohibited objects from heavily cluttered X-ray images. Specifically, we propose a Material Activation (MA) module that cross-scale flows the atomic number Z information through the network to mine material clues and reduce irrelevant information interference in detecting prohibited items. However, collecting atomic number images requires much labor, increasing costs. Therefore, we propose a method to automatically generate atomic number Z images by exploring the color information of X-ray images, which significantly reduces the manual acquisition cost. Extensive experiments demonstrate that our method can accurately and robustly detect prohibited items from heavily cluttered X-ray images. Furthermore, we extensively evaluate our method on HiXray and OPIXray, and the best result is 2.1% mAP50 higher than the state-of-the-art models on HiXray.

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A Lightweight One-Stage Defect Detection Network for Small Object Based on Dual Attention Mechanism and PAFPN

Cited by 29 publications

References 18 publications

A Deep Learning Model for Detecting Cage-Free Hens on the Litter Floor

A Deep Learning Model for Detecting Cage-Free Hens on the Litter Floor

Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey

Atomic number prior guided network for prohibited items detection from heavily cluttered X-ray imagery

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