Lightweight Detection Method for X-ray Security Inspection with Occlusion

Wang, Zanshi; Wang, Xiaohua; Shi, Yueting; Qi, Hang; Jia, Minli; Wang, Weijiang

doi:10.3390/s24031002

Cited by 3 publications

(1 citation statement)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering the complex environment of pigeon coops and the deployment requirements of models, we improved YOLOv8 using C2f-Faster-EMA, C2f-Faster, and Dysample to reduce the interference of complex environments on the model and enhance the model's ability to detect low-resolution and small targets. Wang et al [39] addressed the problem of imbalanced difficulty samples by introducing SlideLoss, but they used a fixed value as the threshold for discriminating difficult samples, which cannot improve the model's generalization ability. We optimized the threshold using exponential moving average (EXPMA) and proposed the EMASlideLoss loss function, effectively improving model performance and enhancing model robustness.…”

Section: Discussionmentioning

confidence: 99%

Improved YOLOv8 Model for Lightweight Pigeon Egg Detection

Jiang,

Zhou,

Xie

et al. 2024

Animals

View full text Add to dashboard Cite

In response to the high breakage rate of pigeon eggs and the significant labor costs associated with egg-producing pigeon farming, this study proposes an improved YOLOv8-PG (real versus fake pigeon egg detection) model based on YOLOv8n. Specifically, the Bottleneck in the C2f module of the YOLOv8n backbone network and neck network are replaced with Fasternet-EMA Block and Fasternet Block, respectively. The Fasternet Block is designed based on PConv (Partial Convolution) to reduce model parameter count and computational load efficiently. Furthermore, the incorporation of the EMA (Efficient Multi-scale Attention) mechanism helps mitigate interference from complex environments on pigeon-egg feature-extraction capabilities. Additionally, Dysample, an ultra-lightweight and effective upsampler, is introduced into the neck network to further enhance performance with lower computational overhead. Finally, the EXPMA (exponential moving average) concept is employed to optimize the SlideLoss and propose the EMASlideLoss classification loss function, addressing the issue of imbalanced data samples and enhancing the model’s robustness. The experimental results showed that the F1-score, mAP50-95, and mAP75 of YOLOv8-PG increased by 0.76%, 1.56%, and 4.45%, respectively, compared with the baseline YOLOv8n model. Moreover, the model’s parameter count and computational load are reduced by 24.69% and 22.89%, respectively. Compared to detection models such as Faster R-CNN, YOLOv5s, YOLOv7, and YOLOv8s, YOLOv8-PG exhibits superior performance. Additionally, the reduction in parameter count and computational load contributes to lowering the model deployment costs and facilitates its implementation on mobile robotic platforms.

show abstract

Section: Discussionmentioning

confidence: 99%

Improved YOLOv8 Model for Lightweight Pigeon Egg Detection

Jiang,

Zhou,

Xie

et al. 2024

Animals

View full text Add to dashboard Cite

show abstract

Using ArcFace Loss Function and Softmax with Temperature Activation Function for Improvement in X-ray Baggage Image Classification Quality

Andriyanov

2024

Mathematics

View full text Add to dashboard Cite

Modern aviation security systems are largely tied to the work of screening operators. Due to physical characteristics, they are prone to problems such as fatigue, loss of attention, etc. There are methods for recognizing such objects, but they face such difficulties as the specific structure of luggage X-ray images. Furthermore, such systems require significant computational resources when increasing the size of models. Overcoming the first and second disadvantage can largely lie in the hardware plane. It needs new introscopes and registration techniques, as well as more powerful computing devices. However, for processing, it is more preferable to improve quality without increasing the computational power requirements of the recognition system. This can be achieved on traditional neural network architectures, but with the more complex training process. A new training approach is proposed in this study. New ways of baggage X-ray image augmentation and advanced approaches to training convolutional neural networks and vision transformer networks are proposed. It is shown that the use of ArcFace loss function for the task of the items binary classification into forbidden and allowed classes provides a gain of about 3–5% for different architectures. At the same time, the use of softmax activation function with temperature allows one to obtain more flexible estimates of the probability of belonging, which, when the threshold is set, allows one to significantly increase the accuracy of recognition of forbidden items, and when it is reduced, provides high recall of recognition. The developed augmentations based on doubly stochastic image models allow one to increase the recall of recognizing dangerous items by 1–2%. On the basis of the developed classifier, the YOLO detector was modified and the mAP gain of 0.72% was obtained. Thus, the research results are matched to the goal of increasing efficiency in X-ray baggage image processing.

show abstract

Fine-YOLO: A Simplified X-ray Prohibited Object Detection Network Based on Feature Aggregation and Normalized Wasserstein Distance

Zhou,

Cao,

et al. 2024

Sensors

View full text Add to dashboard Cite

X-ray images typically contain complex background information and abundant small objects, posing significant challenges for object detection in security tasks. Most existing object detection methods rely on complex networks and high computational costs, which poses a challenge to implement lightweight models. This article proposes Fine-YOLO to achieve rapid and accurate detection in the security domain. First, a low-parameter feature aggregation (LPFA) structure is designed for the backbone feature network of YOLOv7 to enhance its ability to learn more information with a lighter structure. Second, a high-density feature aggregation (HDFA) structure is proposed to solve the problem of loss of local details and deep location information caused by the necked feature fusion network in YOLOv7-Tiny-SiLU, connecting cross-level features through max-pooling. Third, the Normalized Wasserstein Distance (NWD) method is employed to alleviate the convergence complexity resulting from the extreme sensitivity of bounding box regression to small objects. The proposed Fine-YOLO model is evaluated on the EDS dataset, achieving a detection accuracy of 58.3% with only 16.1 M parameters. In addition, an auxiliary validation is performed on the NEU-DET dataset, the detection accuracy reaches 73.1%. Experimental results show that Fine-YOLO is not only suitable for security, but can also be extended to other inspection areas.

show abstract

Lightweight Detection Method for X-ray Security Inspection with Occlusion

Cited by 3 publications

References 38 publications

Improved YOLOv8 Model for Lightweight Pigeon Egg Detection

Improved YOLOv8 Model for Lightweight Pigeon Egg Detection

Using ArcFace Loss Function and Softmax with Temperature Activation Function for Improvement in X-ray Baggage Image Classification Quality

Fine-YOLO: A Simplified X-ray Prohibited Object Detection Network Based on Feature Aggregation and Normalized Wasserstein Distance

Contact Info

Product

Resources

About