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Safety inspection nowadays is an effective means to safeguard public security, which mainly relies on professional security personnel to carry out inspections. In order to detect automatically contraband in X-ray images, a new prohibited item detection method on the strength of the modified YOLOv7 algorithm is present. The spatial attention constructed by large kernel attention was introduced into the lower layer of the YOLOv7 backbone network to extract the remote dependence information and texture information of the lower layer feature map. The proposed method was tested on public X-ray data set for a safety inspection, and the result showed that the improved means can advance the detection accuracy of the model.

X-ray contraband detection plays an important role in the field of public safety. To solve the multi-scale and obscuration problem in X-ray contraband detection, we propose a material-aware path aggregation network to detect and classify contraband in X-ray baggage images. Based on YoloX, our network integrates two new modules: multi-scale smoothed atrous convolution (SCA) and material-aware coordinate attention modules (MCA). In SAC, an improved receptive field-enhanced network structure is proposed by combining smoothed atrous convolution, using separate shared convolution, with a parallel branching structure, which allows for the acquisition of multi-scale receptive fields while reducing grid effects. In the MCA, we incorporate a spatial coordinate separation material perception module with a coordinated attention mechanism. A material perception module can extract the material information features in X and Y dimensions, respectively, which alleviates the obscuring problem by focusing on the distinctive material characteristics. Finally, we design the shape-decoupled SIoU loss function (SD-SIoU) for the shape characteristics of the X-ray contraband. The category decoupling module and the long–short side decoupling module are integrated to the shape loss. It can effectively balance the effect of the long–short side. We evaluate our approach on the public X-ray contraband SIXray and OPIXray datasets, and the results show that our approach is competitive with other X-ray baggage inspection approaches.

In consideration of imaging principle and sampling process of the X-ray imager, the collected images often appear blurry and noisy. We propose to add image processing in the reconstruction process to optimize the 3D reconstruction surface results of X-ray images in this paper. This paper proposes an automatic image data acquisition method to acquire images. According to the characteristics of X-ray images, an image processing method is drawn up. In this paper, the original image and the processed X-ray image are divided into two groups, and three-dimensional reconstruction is performed respectively, and the visualization effect and reconstruction time of the reconstruction result is compared. Experiments show that the running speed of the program is improved, and the visualization results show that the surface texture refinement and the reconstruction structure of the reconstruction effect after image processing have also been well optimized.

Detecting dangerous goods in security images is a challenging task. To overcome the challenges of localization difficulty and directional feature loss of contraband in X-ray images, our proposed solution, R3Det, employs the Convolutional Block Attention Module (CBAM). By integrating ResNeSt into the original detector, our detector includes a soft attention mechanism to redistribute weights among feature channels. This enhances the network’s ability to extract important features and facilitates extraction of target objects features under complex backgrounds. Subsequently, we introduced the spatial and channel attention mechanism during the connection between the backbone and the Feature Pyramid Network (FPN), enabling the model to focus on significant features while ignoring complex background information, then the following Feature Refinement Module to achieve feature alignment in a pixel-by-pixel manner. Our approach successfully achieved rotating target detection in the background of complex X-ray images. Through end-to-end training, our proposed method achieves a 2.6% improvement over the original detector, with a mean Average Precision (mAP) of 86.7%. Notably, our approach showed remarkable results in detecting sensors, pressure, and firetrackers. Now, we have deployed our proposed method on actual security machines for hazardous material detection tasks.

Currently, X-ray inspection systems may produce false detections due to factors such as the varying sizes of contraband images, complex backgrounds, and blurred edges. To address this issue, we propose the YOLO-CID method for contraband image detection. Firstly, we designed the MP-OD module in the backbone network to enhance the model’s ability to extract key information from complex background images. Secondly, at the neck of the network, we designed a simplified version of BiFPN to add cross-scale connection lines in the feature fusion structure, to preserve deeper semantic information and enhance the network’s ability to represent objects in low-contrast or occlusion situations. Finally, we added a new object detection layer to improve the model’s accuracy in detecting small objects in dense environments. Experimental results on the PIDray public dataset show that the average accuracy rate of the YOLO-CID algorithm is 82.7% and the recall rate is 81.2%, which are 4.9% and 3.2% higher than the YOLOv7 algorithm, respectively. At the same time, the mAP on the CLCXray dataset reached 80.2%. Additionally, it can achieve a real-time detection speed of 40 frames per second and 43 frames per second in real scenes. These results demonstrate the effectiveness of the YOLO-CID algorithm in X-ray contraband detection.

To address the shortage of public datasets for customs X-ray images of contraband and the difficulties in deploying trained models in engineering applications, a method has been proposed that employs the Extract-Transform-Load (ETL) approach to create an X-ray dataset of contraband items. Initially, X-ray scatter image data is collected and cleaned. Using Kafka message queues and the Elasticsearch (ES) distributed search engine, the data is transmitted in real-time to cloud servers. Subsequently, contraband data is annotated using a combination of neural networks and manual methods to improve annotation efficiency and implemented mean hash algorithm for quick image retrieval. The method of integrating targets with backgrounds has enhanced the X-ray contraband image data, increasing the number of positive samples. Finally, an Airport Customs X-ray dataset (ACXray) compatible with customs business scenarios has been constructed, featuring an increased number of positive contraband samples. Experimental tests using three datasets to train the Mask Region-based Convolutional Neural Network (Mask R-CNN) algorithm and tested on 400 real customs images revealed that the recognition accuracy of algorithms trained with Security Inspection X-ray (SIXray) and Occluded Prohibited Items X-ray (OPIXray) decreased by 16.3% and 15.1%, respectively, while the ACXray dataset trained algorithm’s accuracy was almost unaffected. This indicates that the ACXray dataset-trained algorithm possesses strong generalization capabilities and is more suitable for customs detection scenarios.

The global illegal wildlife trade has been anecdotally linked to other serious crimes, such as fraud, corruption, and money laundering, as well as the cross-border trafficking of drugs, arms, counterfeit goods, and persons. As research on this topic is scarce and sporadic, we conducted a scoping literature review to gather information across multiple disciplines and evidence types on crime convergences in the illegal wildlife trade. We reviewed 150 papers published between 2000 and 2020. We found that the illegal trade in many of the most frequently trafficked species have reportedly converged with numerous other serious and organised crimes, most commonly drug trafficking. Convergences can occur in a variety of ways, although the diversification of organised crime groups, parallel trafficking of contraband, and use of enabling crimes (such as corruption and violence) were the most frequently described. Possible explanations for our results and future research directions are discussed.

Forensic evidentiary backlogs are indicative of the growing need for cost-effective, high-throughput instrumental methods. One such emerging technology that shows high promise in meeting this demand while also allowing on-site forensic investigation is portable mass spectrometric (MS) instrumentation, particularly that which enables the coupling to ambient ionization techniques. While the benefits of rapid, on-site screening of contraband can be anticipated, the inherent legal implications of field-collected data necessitates that the analytical performance of technology employed be commensurate with accepted techniques. To this end, comprehensive analytical validation studies are required before broad incorporation by forensic practitioners can be considered, and are the focus of this work. Pertinent performance characteristics such as throughput, selectivity, accuracy/precision, method robustness, and ruggedness have been investigated. Reliability in the form of false positive/negative response rates is also assessed, examining the effect of variables such as user training and experience level. To provide flexibility toward broad chemical evidence analysis, a suite of rapidly-interchangeable ion sources has been developed and characterized through the analysis of common illicit chemicals and emerging threats like substituted phenethylamines. Graphical Abstract ᅟ

Deep convolutional networks have been developed to detect prohibited items for automated inspection of X-ray screening systems in the transport security system. To our knowledge, the existing frameworks were developed to recognize threats using only baggage security X-ray scans. Therefore, the detection accuracy in other domains of security X-ray scans, such as cargo X-ray scans, cannot be ensured. We propose an object detection method for efficiently detecting contraband items in both cargo and baggage for X-ray security scans. The proposed network, MFA-net, consists of three plug-and-play modules, including the multiscale dilated convolutional module, fusion feature pyramid network, and auxiliary point detection head. First, the multiscale dilated convolutional module converts the standard convolution of the detector backbone to a conditional convolution by aggregating the features from multiple dilated convolutions using dynamic feature selection to overcome the object-scale variant issue. Second, the fusion feature pyramid network combines the proposed attention and fusion modules to enhance multiscale object recognition and alleviate the object and occlusion problem. Third, the auxiliary point detection head adopts an auxiliary head to predict the new keypoints of the bounding box to emphasize the localizability without requiring further ground-truth information. We tested the performance of the MFA-net on two large-scale X-ray security image datasets from different domains: a Security Inspection X-ray (SIXray) dataset in the baggage domain and our dataset, named CargoX, in the cargo domain. Moreover, MFA-net outperformed state-of-the-art object detectors in both domains. Thus, adopting the proposed modules can further increase the detection capability of the current object detectors on X-ray security images.

X-ray inspections of contraband are widely used to maintain public transportation safety and protect life and property when people travel. To improve detection accuracy and reduce the probability of missed and false detection, a contraband detection algorithm YOLOv8s-DCN-EMA-IPIO* based on YOLOv8s is proposed. Firstly, the super-resolution reconstruction method based on the SRGAN network enhances the original data set, which is more conducive to model training. Secondly, DCNv2 (deformable convolution net v2) is introduced in the backbone network and merged with the C2f layer to improve the ability of the feature extraction and robustness of the model. Then, an EMA (efficient multi-scale attention) mechanism is proposed to suppress the interference of complex background noise and occlusion overlap in the detection process. Finally, the IPIO (improved pigeon-inspired optimization), which is based on the cross-mutation strategy, is employed to maximize the convolutional neural network’s learning rate to derive the optimal group’s weight information and ultimately improve the model’s detection and recognition accuracy. The experimental results show that on the self-built data set, the mAP (mean average precision) of the improved model YOLOv8s-DCN-EMA-IPIO* is 73.43%, 3.98% higher than that of the original model YOLOv8s, and the FPS is 95, meeting the deployment requirements of both high precision and real-time.