A novel enhanced region proposal network and modified loss function: threat object detection in secure screening using deep learning

<title>Abstract</title> Accurate and efficient object detection is essential for intelligent road-scene monitoring systems operating in visually complex and resource-constrained environments. While one-stage detectors achieve high inference speed, they often struggle with precise localization of small or low-contrast objects, whereas two-stage detectors provide higher accuracy at the cost of increased latency. To address this trade-off, this paper proposes a hybrid object-detection architecture that integrates You Only Look Once version 5-Small (YOLOv5s) as a fast proposal generator with Faster Region-Based Convolutional Neural Network (Faster R-CNN) as a region-wise refinement module. The proposed framework replaces the Region Proposal Network of Faster R-CNN with high-confidence YOLOv5s detections and employs confidence-weighted fusion to produce spatially consistent final predictions. The hybrid model was evaluated on complex road-scene data using standard object-detection metrics, including mean Average Precision at IoU 0.50 (mAP@50), precision, recall, and inference speed. Experimental results show that the proposed approach achieves mAP@50 of 0.89, improving upon the YOLOv5s baseline by 4.7 percentage points, while maintaining near–real-time performance at 45 frames per second, which is approximately three times faster than a standalone Faster R-CNN. The hybrid detector also attained a precision of 0.93 and a recall of 0.90, demonstrating improved localization accuracy and reduced false detections, particularly for small and visually ambiguous road-scene objects. Repeated experiments confirmed the robustness of the approach, with consistent accuracy gains and low variance across runs. These results demonstrate that strategically combining one-stage and two-stage detection paradigms can yield a favorable accuracy–efficiency balance, making the proposed hybrid architecture suitable for practical deployment in intelligent road-infrastructure and smart-city applications.

The localization of multiple interference sources in Global Navigation Satellite Systems (GNSS) can be achieved using carrier-to-noise ratio (C/N0) information provided by GNSS receivers, such as those embedded in smartphones. However, in increasingly prevalent complex scenarios—such as the coexistence of multiple directional interferences, increased diversity and density of GNSS interference, and the presence of multiple low-power interference sources—conventional localization methods often fail to provide reliable results, thereby limiting their applicability in real-world environments. This paper presents a multi-interference sources localization method using object detection in GNSS C/N0 distribution maps. The proposed method first exploits the similarity between C/N0 data reported by GNSS receivers and image grayscale values to construct C/N0 distribution maps, thereby transforming the problem of multi-source GNSS interference localization into an object detection and localization task based on image processing techniques. Subsequently, an Oriented Squeeze-and-Excitation-based Faster Region-based Convolutional Neural Network (OSF-RCNN) framework is proposed to process the C/N0 distribution maps. Building upon the Faster R-CNN framework, the proposed method integrates an Oriented RPN (Region Proposal Network) to regress the orientation angles of directional antennas, effectively addressing their rotational characteristics. Additionally, the Squeeze-and-Excitation (SE) mechanism and the Feature Pyramid Network (FPN) are integrated at key stages of the network to improve sensitivity to small targets, thereby enhancing detection and localization performance for low-power interference sources. The simulation results verify the effectiveness of the proposed method in accurately localizing multiple interference sources under the increasingly prevalent complex scenarios described above.

In the past few years, automatic building detection in aerial images has become an emerging field in computer vision. Detecting the specific types of houses will provide information in urbanization, change detection, and urban monitoring that play increasingly important roles in modern city planning and natural hazard preparedness. In this paper, we demonstrate the effectiveness of detecting various types of houses in aerial imagery using Faster Region-based Convolutional Neural Network (Faster-RCNN). After formulating the dataset and extracting bounding-box information, pre-trained ResNet50 is used to get the feature maps. The fully convolutional Region Proposal Network (RPN) first predicts the bounds and objectness score of objects (in this case house) from the feature maps. Then, the Region of Interest (RoI) pooling layer extracts interested regions to detect objects that are present in the images. To the best of our knowledge, this is the first attempt at detecting houses using Faster R-CNN that has achieved satisfactory results. This experiment opens a new path to conduct and extent the works not only for civil and environmental domain but also other applied science disciplines.

To reduce environmental pollution caused by improper waste disposal, efficient waste management solutions are essential. Traditional methods rely on manual monitoring, which is time-consuming and ineffective. Advancements in artificial intelligence and deep learning enable automated waste detection and localization, improving efficiency in waste management. Faster R-CNN, an advanced object detection model, enhances precision in recognizing and localizing waste in visual data. By utilizing deep learning techniques, waste detection achieves high accuracy, reducing dependence on manual inspections.The integration of Faster R-CNN with image processing techniques allows for real-time identification of waste objects in complex environments. Localization is achieved by drawing bounding boxes around detected waste, providing precise positional information. The Region Proposal Network (RPN) plays a key role in this process by generating candidate object regions through anchor boxes and refining them based on object scores. Tensor Flow’s Object Detection API facilitates model training and optimization, ensuring accurate recognition of waste materials. High-performance convolutional neural networks enhance feature extraction, distinguishing waste from non-waste objects effectively. The use of deep learning in image-based waste detection supports scalable waste management solutions.Automated waste detection and localization contribute to sustainable urban development by enabling proactive waste management strategies. The ability to accurately identify and mark waste locations enhances the efficiency of cleaning processes and reduces environmental hazards. By analyzing waste distribution patterns, authorities can optimize resource allocation and improve waste disposal planning. The adoption of AI-driven approaches in waste detection minimizes human effort while promoting a cleaner and healthier environment. Key Words: AI-Driven waste detection, Faster R-CNN, Region Proposal Network, TensorFlow object detection API, Image processing and Localization.

Computer vision is developing really fast in recent years, and object detection is now one of the hottest topics. faster region-based convolutional neural network (faster R-CNN) and You Only Look Once (YOLOv3) are two popular modules to solve object detection problems, but they are implemented in different ways and thus have different performance in practice. This article is going to introduce these two modern modules and make a few experiments to compare the performance of each module with various datasets. This analysis will focus on speed, accuracy and the performances in different situations. Two specific studies will be mentioned as two typical examples of object detection application: face mask detection and greenhouse detection from satellite images. At last, the article will draw out a conclusion, make some suggestions for the choice of faster R-CNN and YOLOv3 and make a prospect for the future. It turns out that generally faster R-CNN fits tasks that require high accuracy better and YOLOv3 can realize real-time detection tasks.

Foreign objects such as packaging bags on the road pose a significant threat to driving safety, especially at high speeds or under low-visibility conditions. However, research on detecting road packaging bags remains limited, and existing object detection models face challenges in small object detection, computational efficiency, and embedded deployment. To address these issues, the lightweight deep learning model RGE-YOLO is the foundation for the real-time detection technique proposed in this contribution. Built upon YOLOv8s, RGE-YOLO incorporates RepViTBlock, Grouped Spatial Convolution (GSConv), and Efficient Multi-Scale Attention (EMA) to optimize computational efficiency, model stability, and detection accuracy. GSConv reduces redundant computations, enhancing model lightweight; EMA enhances the model’s ability to capture multi-scale information by integrating channel and spatial attention mechanisms; RepViTBlock integrates convolution and self-attention mechanisms to improve feature extraction capabilities. The proposed method was validated on a custom-built road plastic bag dataset comprising 6,000 augmented images. Experimental results demonstrate that RGE-YOLO outperforms state-of-the-art models such as Single Shot MultiBox Detector (SSD) and Faster Region-based Convolutional Neural Network (Faster R-CNN) in terms of mean average precision (mAP 92.2%) and detection speed (250 FPS), while significantly reducing model parameters (9.1 M) and computational complexity (23.9 GFLOPs), increasing its suitability for installation on computerized systems within vehicles. It introduces an effective and lightweight approach for detecting road packaging bags and contributes to increased driving safety.

In the last couple of years, advancements in the deep learning, especially in convolutional neural networks, proved to be a boon for the image classification and recognition tasks. One of the important practical applications of object detection and image classification can be for security enhancement. If dangerous objects or scenes can be identified automatically, then a lot of accidents can be prevented. For this purpose, in this paper we made use of state-of-the-art implementation of Faster Region-based Convolutional Neural Network (Faster R-CNN) based on the monitoring video of hoisting sites to train a model to detect the dangerous object and the worker. By extracting the locations of them, object-human interactions during hoisting, mainly for changes in their spatial location relationship, can be understood whereby estimating whether the scene is safe or dangerous. Experimental results showed that the pre-trained model achieved good performance with a high mean average precision of 97.66% on object detection and the proposed method fulfilled the goal of dangerous scenes recognition perfectly.

This paper describes the use of two different deep-learning algorithms for object detection to recognize different badgers. We use recordings of four different badgers under varying background illuminations. In total four different object detection algorithms based on deep neural networks are compared: The single shot multi-box detector (SSD) with the Inception-V2 or MobileNet as a backbone, and the faster region-based convolutional neural network (Faster R-CNN) combined with Inception-V2 or residual networks. Furthermore, two different activation functions are compared to compute probabilities that some badger is in the detected region: the softmax and sigmoid functions. The results of all eight models show that SSD obtains higher recognition accuracies (97.8%–98.6%) than Faster R-CNN (84.8%–91.7%). However, the training time of Faster R-CNN is much shorter than that of SSD. The use of different output activation functions seems not to matter much.

The detection of person-borne threat objects, such as improvised explosive devices, at a safe distance is an ongoing challenge. While much attention has been given to other parts of the electromagnetic spectrum, very little is known about what potential exists to detect clothing obscured threats over the ultraviolet through the shortwave-infrared spectral region. Hyperspectral imaging may provide a greater ability to discriminate between target and non-target by using the full spectrum. This study investigates this potential by the collection and analysis of hyperspectral images of obscured proxy threat objects. The results of this study indicate a consistent ability to detect the presence of concealed objects. The study included the use of VNIR (400 nm to 1000 nm) and SWIR (1000 nm to 1700 nm), as defined here, hyperspectral imagers. Both spectral ranges provided comparable results, however, potential advantages of the SWIR spectral region are discussed.

Due to motorcycles or vehicles, sudden accidents often occur when passengers disembark from buses at bus stops. These crashes may often cause risks to passengers. In this paper, we present a vision-based threat object (TO) detection and passenger safety system that helps bus drivers and ensures passengers safety when getting off and on buses. In these systems, the fast detection of threat objects is the main priority. Therefore, we also propose a fast motion analysis algorithm based on subtracted images using the change values of the brightness difference of the pixels corresponding to the boundary of a segmented object. We configure a secondary camera on the back of the bus to detect oncoming objects, and a primary camera up to the door to analyze objects' status and track TOs. TO information is transmitted to the control unit, and the control unit controls the door's status or triggers alarms with an on-board speaker to provide a danger signal to passengers. We set up our system on a bus and verified it in a real road environment.

Algorithms based on deep learning are taking off as solutions to several problems in numerous fields. Numerous deep learning architectures like convolutional neural networks, recurrent neural networks, deep belief networks, artificial neural networks and deep neural networks have been used in fields such as audio recognition, machine vision, processing of natural language, recognition of speech, social network filtering, computer vision, translating languages and bioinformatics. One such application of deep learning architectures is object detection. Object detection in satellite and aerial imagery undertakes an important role in a long list of applications and has allured a lot of attention which resulted in exceptional progress in recent years. It has abundant uses in various fields such as surveillance, military, national security, urban planning, environmental applications, traffic control and forestry. Several issues like varying size, background and orientation of the target object make the automatic detection of target in satellite imagery a challenging issue. Recent research in this field has given better speed and accuracy to the detection of the target object. With the rise in the data being generated by the Earth Observation programs, it is becoming much more imperative to find better and efficient ways to exploit this data by improving upon the existing Deep Learning research. In this paper, we bring forth a wide-ranging analysis of the recent advancements in methods of object detection based on deep learning to recognize the objects in satellite imagery.KeywordsDeep learningObject detectionSatellite imageryRemote sensingYou Only Look Once (YOLO)You Only Look Twice (YOLT)Single Shot Detector (SSD)Convolutional Neural Networks (CNN)Faster Region-based Convolutional Neural Networks (Faster RCNN)Hybrid Deep Convolutional Neural Network (HDNN)Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN)

Detection of metastasis of mediastinal lymph nodes in lung cancer patients with an artificial intelligence model.

Deep learning has been successfully applied in classification of white blood cells (WBCs), however, accuracy and processing time are found to be less than optimal hindering it from getting its full potential. This is due to imbalanced dataset, intra-class compactness, inter-class separability and overfitting problems. The main research idea is to enhance the classification and prediction accuracy of blood images while lowering processing time through the use of deep convolutional neural network (DCNN) architecture by using the modified loss function. The proposed system consists of a deep neural convolution network (DCNN) that will improve the classification accuracy by using modified loss function along with regularization. Firstly, images are pre-processed and fed through DCNN that contains different layers with different activation function for the feature extraction and classification. Along with modified loss function with regularization, weight function aids in the classification of WBCs by considering weights of samples belonging to each class for compensating the error arising due to imbalanced dataset. The processing time will be counted by each image to check the time enhancement. The classification accuracy and processing time are achieved using the dataset-master. Our proposed solution obtains better classification performance in the given dataset comparing with other previous methods. The proposed system enhanced the classification accuracy of 98.92% from 96.1% and a decrease in processing time from 0.354 to 0.216 s. Less time will be required by our proposed solution for achieving the model convergence with 9 epochs against the current convergence time of 13.5 epochs on average, epoch is the formation white blood cells (WBCs) and the development of granular cells. The proposed solution modified loss function to solve the adverse effect caused due to imbalance dataset by considering weight and use regularization technique for overfitting problem.

The use of millimetre-wave imaging to identify threat objects such as guns and bomb material concealed on the person is well documented. However, the technology has been hindered by the performance and cost barriers typically associated with imaging at mm-wave frequencies. A novel scanning technique that minimises the receiver count while operating at very high efficiency levels has made it possible to build a cost-effective and high-performance mm-wave imager that can make security screening a commercial reality. The imager design allows for either passive or active operation and its compact form factor is suitable for practical installation in security channel situations. The uses of this technology include portal screening of personnel for high-resolution imaging of concealed threat objects or longer distance surveillance type monitoring of checkpoints and crowds. This presentation details the use of the imager in an active configuration to observe a checkpoint or crowd scene at stand-off distances of up to 50 metres. Target objects to be detected are the hidden metal components associated with suicide bomb constructions. A typical bomb consists of several explosive filled pipes strapped to the body or clusterings of small metallic objects embedded in explosives. Trials at 94GHz have yielded positive results by showing the presence of concealed metallic objects on people at distances of 25 metres. Objects detected have included simulated bomb constructions such as groups of metal pipes and clusters of nuts and bolts. These tests have been conducted using a Gunn based CW source and direct detect receiver unit. Further enhancement of the system includes the use of an FMCW front-end configuration.

ABSTRACTOil tank detection is a challenging task, primarily due to high time-consumption. This paper aims at further investigating this challenge and proposes a new hierarchical approach to detect oil tanks, especially with respect to how false alarm rates are reduced. The proposed approach is divided into four stages: region of interest (ROI) extraction, circular object detection, feature extraction, and classification. The first stage, which is a key component of this approach to reduce false alarm and processing time, is applied by an improved faster region-based convolutional neural network (Faster R-CNN) to extract oil depots. In the second stage, a number of candidate objects of the target are selected from the extracted ROIs by a fast circle detection method. Afterwards, in the third stage, a robust feature extractor based on a combination of the output feature vectors from convolutional neural network (CNN), as a high-level feature extractor, and histogram of oriented gradients (HOG), as a low-level feature extractor, are used for representing features of various targets. Finally, the support vector machine (SVM) is employed for classification. The experimental results confirm that the proposed approach has good prediction accuracy and is able to reduce the false alarm rates.