Research of recognition accuracy of dangerous and safe x-ray baggage images using neural network transfer learning

Andriyanov, Nikita; Volkov, Al K.; Volkov, An K.; Гладких, А. А.

doi:10.1088/1757-899x/1061/1/012002

Cited by 7 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They were categorized into 5 small, 26 medium, and 7 large objects. Among the small objects, USB flash drives are sensitive storage media linked closely to the leakage of confidential information, whereas bullets, nail clippers, batteries, and lighters are objects that must be detected for flight safety [24][25][26]. Four types of data are provided according to their provision methods, as shown in Table 2.…”

Section: Definition Of Dataset and Object Sizementioning

confidence: 99%

SHOMY: Detection of Small Hazardous Objects using the You Only Look Once Algorithm

2022

KSII TIIS

View full text Add to dashboard Cite

Research on the advanced detection of harmful objects in airport cargo for passenger safety against terrorism has increased recently. However, because associated studies are primarily focused on the detection of relatively large objects, research on the detection of small objects is lacking, and the detection performance for small objects has remained considerably low. Here, we verified the limitations of existing research on object detection and developed a new model called the Small Hazardous Object detection enhanced and reconstructed Model based on the You Only Look Once version 5 (YOLOv5) algorithm to overcome these limitations. We also examined the performance of the proposed model through different experiments based on YOLOv5, a recently launched object detection model. The detection performance of our model was found to be enhanced by 0.3 in terms of the mean average precision (mAP) index and 1.1 in terms of mAP (.5:.95) with respect to the YOLOv5 model. The proposed model is especially useful for the detection of small objects of different types in overlapping environments where objects of different sizes are densely packed. The contributions of the study are reconstructed layers for the Small Hazardous Object detection enhanced and reconstructed Model based on YOLOv5 and the non-requirement of data preprocessing for immediate industrial application without any performance degradation.

show abstract

Section: Definition Of Dataset and Object Sizementioning

confidence: 99%

SHOMY: Detection of Small Hazardous Objects using the You Only Look Once Algorithm

2022

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…This complicates the task of analyzing such images. In the literature, there are works on the recognition of objects in X-ray images of luggage [7][8][9], but they consider that the image contains an object of any single class in the full image. Some solution to the problem of prohibited item detection is presented in [10].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Detecting Dangerous Objects in X-rays of Luggage

Andriyanov

2023

Intels’22

View full text Add to dashboard Cite

show abstract

“…This human-ML collaboration is seen in a variety of fields, from medical diagnosis and treatment [1,2,3], to automated driving systems [4,5], to threat detection [6]. ML assistance has also been utilized in aiding visual search and predicting the presence of targets in modalities such as baggage screens [7,8,9] and simulated combat images [10]. In the field of nuclear safeguards, ML diagnostic tools could also be applied to assist human users and enhance performance [11,12].…”

Section: Introductionmentioning

confidence: 99%

The Cognitive Effects of Machine Learning Aid in Domain-Specific and Domain-General Tasks

Divis¹,

Howell²,

Matzen³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

With machine learning (ML) technologies rapidly expanding to new applications and domains, users are collaborating with artificial intelligence-assisted diagnostic tools to a larger and larger extent. But what impact does ML aid have on cognitive performance, especially when the ML output is not always accurate? Here, we examined the cognitive effects of the presence of simulated ML assistance-including both accurate and inaccurate output-on two tasks (a domain-specific nuclear safeguards task and domain-general visual search task). Patterns of performance varied across the two tasks for both the presence of ML aid as well as the category of ML feedback (e.g., false alarm). These results indicate that differences such as domain could influence users' performance with ML aid, and suggest the need to test the effects of ML output (and associated errors) in the specific context of use, especially when the stimuli of interest are vague or ill-defined.

show abstract

Research of recognition accuracy of dangerous and safe x-ray baggage images using neural network transfer learning

Cited by 7 publications

References 12 publications

SHOMY: Detection of Small Hazardous Objects using the You Only Look Once Algorithm

SHOMY: Detection of Small Hazardous Objects using the You Only Look Once Algorithm

Deep Learning for Detecting Dangerous Objects in X-rays of Luggage

The Cognitive Effects of Machine Learning Aid in Domain-Specific and Domain-General Tasks

Contact Info

Product

Resources

About