Automated firearms detection in cargo x-ray images using RetinaNet

Cui, Yunqi; Oztan, Basak

doi:10.1117/12.2517817

Cited by 17 publications

(10 citation statements)

References 4 publications

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“…In addition to all the above, there is a recently developed CNN-based detector called RetinaNet [45]. RetinaNet was designed to solve the problem of having extreme foreground-background class imbalanced problems and has been also applied to X-ray images [46].…”

Section: Handgun Detectormentioning

confidence: 99%

Deep autoencoder for false positive reduction in handgun detection

Vállez

Velasco-Mata

Déniz

2020

Neural Comput & Applic

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In an object detection system, the main objective during training is to maintain the detection and false positive rates under acceptable levels when the model is run over the test set. However, this typically translates into an unacceptable rate of false alarms when the system is deployed in a real surveillance scenario. To deal with this situation, which often leads to system shutdown, we propose to add a filter step to discard part of the new false positive detections that are typical of the new scenario. This step consists of a deep autoencoder trained with the false alarm detections generated after running the detector over a period of time in the new scenario. Therefore, this step will be in charge of determining whether the detection is a typical false alarm of that scenario or whether it is something anomalous for the autoencoder and, therefore, a true detection. In order to decide whether a detection must be filtered, three different approaches have been tested. The first one uses the autoencoder reconstruction error measured with the mean squared error to make the decision. The other two use the k-NN (k-nearest neighbors) and one-class SVMs (support vector machines) classifiers trained with the autoencoder vector representation. In addition, a synthetic scenario has been generated with Unreal Engine 4 to test the proposed methods in addition to a dataset with real images. The results obtained show a reduction in the number of false positives between 22.5% and 87.2% and an increase in the system’s precision of 1.2%$$-47$$ - 47 % when the autoencoder is applied.

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Section: Handgun Detectormentioning

confidence: 99%

Deep autoencoder for false positive reduction in handgun detection

Vállez

Velasco-Mata

Déniz

2020

Neural Comput & Applic

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“…RetinaNet, a recently proposed powerful object detection framework, which surpasses the detection performance of cutting-edge, two-stage R-CNN family object detectors and matches the speed of onestage object detectors, appears to be the most prospective for livestock face detection. In the previous research, RetinaNet was used to explore for detection of road damages [37], automated detection of firearms in cargo X-ray images [38], and the task of indoor assistance navigation for blind and visually impaired persons [39]. Despite the general appeal of RetinaNet, it has not been evaluated in great detail for precision livestock monitoring practices.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Deep Learning for Automatic Multi-View Face Detection in Cattle

Wang

Guo

et al. 2021

Agriculture

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Individual identification plays an important part in disease prevention and control, traceability of meat products, and improvement of agricultural false insurance claims. Automatic and accurate detection of cattle face is prior to individual identification and facial expression recognition based on image analysis technology. This paper evaluated the possibility of the cutting-edge object detection algorithm, RetinaNet, performing multi-view cattle face detection in housing farms with fluctuating illumination, overlapping, and occlusion. Seven different pretrained CNN models (ResNet 50, ResNet 101, ResNet 152, VGG 16, VGG 19, Densenet 121 and Densenet 169) were fine-tuned by transfer learning and re-trained on the dataset in the paper. Experimental results showed that RetinaNet incorporating the ResNet 50 was superior in accuracy and speed through performance evaluation, which yielded an average precision score of 99.8% and an average processing time of 0.0438 s per image. Compared with the typical competing algorithms, the proposed method was preferable for cattle face detection, especially in particularly challenging scenarios. This research work demonstrated the potential of artificial intelligence towards the incorporation of computer vision systems for individual identification and other animal welfare improvements.

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“…Researchers have widely used data augmentation to overcome the problem caused by insufficient training data. In previous research, for example, data augmentation was performed using classical image processing techniques for cargo inspection [5][6][7][8][9][10][11]. Objects to be detected, such as prohibited items, were cropped from the given X-ray image, the background was removed, and the extracted object was synthesized into an arbitrary container image to create a synthesized image [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…In previous research, for example, data augmentation was performed using classical image processing techniques for cargo inspection [5][6][7][8][9][10][11]. Objects to be detected, such as prohibited items, were cropped from the given X-ray image, the background was removed, and the extracted object was synthesized into an arbitrary container image to create a synthesized image [5][6][7][8][9][10]. This method can be useful for detecting objects in various backgrounds in the real environment.…”

Section: Introductionmentioning

confidence: 99%

Data Augmentation of Backscatter X-ray Images for Deep Learning-Based Automatic Cargo Inspection

Cho

Park

Kim

et al. 2021

Sensors

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Custom inspection using X-ray imaging is a very promising application of modern pattern recognition technology. However, the lack of data or renewal of tariff items makes the application of such technology difficult. In this paper, we present a data augmentation technique based on a new image-to-image translation method to deal with these difficulties. Unlike the conventional methods that convert a semantic label image into a realistic image, the proposed method takes a texture map with a special modification as an additional input of a generative adversarial network to reproduce domain-specific characteristics, such as background clutter or sensor-specific noise patterns. The proposed method was validated by applying it to backscatter X-ray (BSX) vehicle data augmentation. The Fréchet inception distance (FID) of the result indicates the visual quality of the translated image was significantly improved from the baseline when the texture parameters were used. Additionally, in terms of data augmentation, the experimental results of classification, segmentation, and detection show that the use of the translated image data, along with the real data consistently, improved the performance of the trained models. Our findings show that detailed depiction of the texture in translated images is crucial for data augmentation. Considering the comparatively few studies that have examined custom inspections of container scale goods, such as cars, we believe that this study will facilitate research on the automation of container screening, and the security of aviation and ports.

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Automated firearms detection in cargo x-ray images using RetinaNet

Cited by 17 publications

References 4 publications

Deep autoencoder for false positive reduction in handgun detection

Deep autoencoder for false positive reduction in handgun detection

Evaluation of Deep Learning for Automatic Multi-View Face Detection in Cattle

Data Augmentation of Backscatter X-ray Images for Deep Learning-Based Automatic Cargo Inspection

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