Baggage screening is crucial for airport security. This paper examines various algorithms for firearm detection in X-ray images of baggage. The focus is on identifying steel barrel bores, which are essential for deton...Baggage screening is crucial for airport security. This paper examines various algorithms for firearm detection in X-ray images of baggage. The focus is on identifying steel barrel bores, which are essential for detonation. For this, the study uses a set of 22,000 X-ray scanned images. After preprocessing with filtering techniques to improve image quality, deep learning methods, such as Convolutional Neural Networks (CNNs), are applied for classification. The results are also compared with Autoencoder and Random Forest algorithms. The results are validated on a second dataset, highlighting the advantages of the adopted approach. Baggage screening is a very important part of the risk assessment and security screening process at airports. Automating the detection of dangerous objects from passenger baggage X-ray scanners can speed up and increase the efficiency of the entire security procedure.展开更多
With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation security.Although X-ray baggage monitoring is...With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation security.Although X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger privacy.To address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation frameworks.However,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset discrepancies.Hence,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as C-BLX.The research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage scans.The proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage scans.More specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage scans.The multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign classes.The contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation results.The proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,respectively.Furthermore,the limitations of the proposed system in extracting precise region segments in intricate noisy settings and potential strategies for overcoming them through post-processing techniques were explored(source code will be available at https://github.com/Divs1159/CNN_BLS.)展开更多
By using baggage tags with embedded EPC Gen 2 tags,the airport has increased the number of bags it can process by 5 percent. May 12,2009—Hong Kong International Airport(HKIA), which serves approximately 48 million pa...By using baggage tags with embedded EPC Gen 2 tags,the airport has increased the number of bags it can process by 5 percent. May 12,2009—Hong Kong International Airport(HKIA), which serves approximately 48 million passengers per year on flights to 150 locations,is now using RFID baggage tags for 100 percent of the 40,000 bags that leave the airport every day.HKIA has upgraded its former bar-code-based system with radio frequency identification, at a cost of HK$50 million(US$6.5 million).展开更多
This study aims to study the conditions for the architectural form of vernacular houses of Thai Korat,Laotian,and Tai Yuan ethnic groups living in the central Lamtakong watershed.Nineteen stilt houses with the age of ...This study aims to study the conditions for the architectural form of vernacular houses of Thai Korat,Laotian,and Tai Yuan ethnic groups living in the central Lamtakong watershed.Nineteen stilt houses with the age of fifty-three to one hundred years were incorporated in the case study.Data were meticulously gathered through methods such as observation,photography,surveying,architectural drawing,three-dimensional modeling,and interviews.The analysis,conducted within the frameworks of ethnic identity and cultural diffusion,involved morphological and comparative assessments.The findings showed that the houses in the present case study could maintain their ethnic identities as can be clearly seen in the space planning and the shapes of the houses passed down from generation to generation.In addition,there was cultural acceptance among these ethnic groups through exchanging,adopting,and borrowing house construction techniques,in order to express the common traits in the larger social context in a friendly and smoother way.This phenomenon indicates that the co-existence in a multicultural society is the key that makes different ethnic groups be able to maintain their ethnic identity and live with the larger society in a friendly way.Hence,the cultural significance of stilt vernacular houses in the study area is embedded in the dynamic process of exchanging house construction techniques,fostering harmony within the broader social context.This preservation simultaneously safeguards the essential elements and key attributes of ethnic identity in architecture.展开更多
We utilize the Novikov-Natterer algorithm for non-uniform attenuation to invert the backscatter projections formed by the scatter of X-rays. The backscatter signal is treated as an emitter in a non-uniformly attenuati...We utilize the Novikov-Natterer algorithm for non-uniform attenuation to invert the backscatter projections formed by the scatter of X-rays. The backscatter signal is treated as an emitter in a non-uniformly attenuating medium. This type of tomography has applications in radiology and dentistry for which metals effectively block the transmission of X-rays. Scanning for metals also has applications in security/baggage screening. The results show that when the forward scattering angle is zero, the algorithm, with a redefinition of the density function f, reduces to the PET attenuation correction.展开更多
文摘Baggage screening is crucial for airport security. This paper examines various algorithms for firearm detection in X-ray images of baggage. The focus is on identifying steel barrel bores, which are essential for detonation. For this, the study uses a set of 22,000 X-ray scanned images. After preprocessing with filtering techniques to improve image quality, deep learning methods, such as Convolutional Neural Networks (CNNs), are applied for classification. The results are also compared with Autoencoder and Random Forest algorithms. The results are validated on a second dataset, highlighting the advantages of the adopted approach. Baggage screening is a very important part of the risk assessment and security screening process at airports. Automating the detection of dangerous objects from passenger baggage X-ray scanners can speed up and increase the efficiency of the entire security procedure.
基金supported by research funds from Khalifa University,No.CIRA-2021-052the Advanced Technology Research Center Program(ASPIRE),No.AARE20-279.
文摘With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation security.Although X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger privacy.To address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation frameworks.However,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset discrepancies.Hence,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as C-BLX.The research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage scans.The proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage scans.More specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage scans.The multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign classes.The contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation results.The proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,respectively.Furthermore,the limitations of the proposed system in extracting precise region segments in intricate noisy settings and potential strategies for overcoming them through post-processing techniques were explored(source code will be available at https://github.com/Divs1159/CNN_BLS.)
文摘By using baggage tags with embedded EPC Gen 2 tags,the airport has increased the number of bags it can process by 5 percent. May 12,2009—Hong Kong International Airport(HKIA), which serves approximately 48 million passengers per year on flights to 150 locations,is now using RFID baggage tags for 100 percent of the 40,000 bags that leave the airport every day.HKIA has upgraded its former bar-code-based system with radio frequency identification, at a cost of HK$50 million(US$6.5 million).
基金This work(Grant No.RGNS 63-097)was supported byOffice of the Permanent Secretary,Ministry of Higher Education,Science,Research and Innovation(OPS MHESl),Thailand,Thailand Science Research and Innovation(TSRI),Thailand,Rajamangala University of Technology Tawan-ok(RMUTTO),Thailand.
文摘This study aims to study the conditions for the architectural form of vernacular houses of Thai Korat,Laotian,and Tai Yuan ethnic groups living in the central Lamtakong watershed.Nineteen stilt houses with the age of fifty-three to one hundred years were incorporated in the case study.Data were meticulously gathered through methods such as observation,photography,surveying,architectural drawing,three-dimensional modeling,and interviews.The analysis,conducted within the frameworks of ethnic identity and cultural diffusion,involved morphological and comparative assessments.The findings showed that the houses in the present case study could maintain their ethnic identities as can be clearly seen in the space planning and the shapes of the houses passed down from generation to generation.In addition,there was cultural acceptance among these ethnic groups through exchanging,adopting,and borrowing house construction techniques,in order to express the common traits in the larger social context in a friendly and smoother way.This phenomenon indicates that the co-existence in a multicultural society is the key that makes different ethnic groups be able to maintain their ethnic identity and live with the larger society in a friendly way.Hence,the cultural significance of stilt vernacular houses in the study area is embedded in the dynamic process of exchanging house construction techniques,fostering harmony within the broader social context.This preservation simultaneously safeguards the essential elements and key attributes of ethnic identity in architecture.
文摘We utilize the Novikov-Natterer algorithm for non-uniform attenuation to invert the backscatter projections formed by the scatter of X-rays. The backscatter signal is treated as an emitter in a non-uniformly attenuating medium. This type of tomography has applications in radiology and dentistry for which metals effectively block the transmission of X-rays. Scanning for metals also has applications in security/baggage screening. The results show that when the forward scattering angle is zero, the algorithm, with a redefinition of the density function f, reduces to the PET attenuation correction.
