PEDESTRIAN RED LIGHT TRAFFIC RECOGNITION MODEL BASED ON YOLOV8 ALGORITHM

Unmanned aerial vehicles (UAVs) often operate under stringent resource constraints while requiring real-time object detection, which can lead to failures in cluttered backgrounds or when targets are small or partially occluded. To address these challenges, we introduce AAB-FusionNet, a real-time detection model specifically designed for UAV edge computing platforms. At its core is the Adaptive Attention Block (AAB), which employs an Adaptive Saliency-based Attention (ASA) mechanism to highlight the most discriminative tokens while a lightweight MBConv sub-layer refines local spatial features. This saliency-driven framework ensures the network remains focused on critical cues despite complex aerial imagery. To further boost performance, AAB-FusionNet utilizes a Multi-layer Feature Fusion Network that integrates three key components: Attentive Inverted Bottleneck Aggregation (AIBA) to restore significant details at multiple scales, DySample for preserving spatial fidelity during feature alignment, and the Dual-Attention Noise Mitigation (DNM) module to suppress environmental noise through complementary channel and spatial attention. Experiments on diverse aerial datasets confirm that AAB-FusionNet achieves robust detection, especially for small or partially occluded objects, while offering real-time inference on low-power hardware. Overall, AAB-FusionNet effectively balances accuracy, computational efficiency, and adaptability, making it ideally suited for UAV scenarios demanding fast, reliable object detection and robust and consistent performance.•Incorporates an Adaptive Saliency-based Attention mechanism to emphasize critical visual cues.•Introduces a Multi-layer Feature Fusion Network for detail restoration, feature alignment, and noise mitigation.•Demonstrates real-time, high-accuracy detection on low-power UAV platforms, particularly for small or occluded targets.

Critical industrial infrastructures are currently facing increasing cyberspace threats in their underlying information and communication systems. The advanced monitoring, control, and management functionalities of the industrial systems firmly rely on the reliable and secure operations of the industrial control system (ICS) network. This article characterizes the ICS network traffic and presents a scalable and efficient solution for real-time ICS network traffic anomaly detection, considering various forms of ICS anomaly events. The events due to the cyberattacks, malicious operating behaviors, and network anomalies can be effectively detected without sophisticated computational requirements and retrieval of communication protocols. The proposed hybrid statistical-machine learning model integrates a seasonal autoregressive integration moving average (SARIMA)-based dynamic threshold model and a long short-term memory (LSTM) model to jointly identify the abnormal traffic patterns with low false omission rates. The proposed solution is extensively evaluated at a realistic ICS cyber–physical system (CPS) testbed, and the numerical results confirm its high detection accuracy and low computational complexity. Note to Practitioners—This article was motivated by the challenge of real-time anomaly detection in industrial cyber–physical systems (CPSs). The existing industrial control system (ICS) network anomaly detection solutions are generally carried out based on a single model based on the historian database and cannot dynamically classify the abnormal conditions in a real-time fashion. A novel hybrid statistical-machine learning model is developed that integrates a seasonal autoregressive integration moving average (SARIMA)-based dynamic threshold model and a long short-term memory (LSTM) model to jointly identify the anomalous events through traffic pattern analysis. The proposed anomaly detection solution can efficiently provide accurate detection for cyberattacks, malicious operating behaviors, and network anomalies while meeting the real-time requirements of ICS networks. The proposed solution can be deployed in the realistic ICS CPSs, e.g., the power generation system, gas pipeline systems, and urban railway transportation systems. The preliminary numerical results obtained from the ICS-CPS testbed suggested that it can provide high detection accuracy with low computational complexity and, hence, can be adopted with minimal deployment hurdles.

With the rapid growth of global maritime traffic, effective ship detection from satellite and aerial images has become essential for applications in maritime security, disaster response, and environmental monitoring. Traditional ship detection methods struggle with the challenges posed by the vastness, complexity, and dynamic nature of oceanic environments. To address these challenges, we propose a highly efficient model that combines the power of CNN with the RPN for fast and accurate ship detection. This study introduces a novel approach for real-time ship object detection in RSI images using the Faster R-CNN. The proposed model is optimized for real-time processing, ensuring the rapid detection of ships even in large-scale satellite imagery. By utilizing a comprehensive dataset of high-resolution RSI images, the system is trained to recognize ships under varying conditions, including different angles, lighting, and sea states. The Faster R-CNN model demonstrates superior performance, offering both high detection accuracy and fast inference times, making it suitable for deployment in real-world maritime surveillance systems. The proposed approach outperformed more conventional R-CNN models in terms of accuracy (99.25 percent for ResNet-50) in experiments conducted on the ship object benchmark dataset.

Currently, the existing fault detection methods have problems in that the detection results are not in line with reality and the detection accuracy is too low when detecting the grounding circulation fault of transmission cables. This paper introduces the wavelet energy spectrum of module components to design and study the real-time fault detection method of grounding the circulating current of the transmission cable. In this study, the wavelet-based detection function that meets the requirements of fault detection is selected. On this basis, the traveling wave fault features are extracted by using the wavelet energy spectrum of the module component. Finally, the fault detection signal is decomposed in real-time, and the fault detection is realized according to the practical value of the reconstructed signal. The comparative experiment proves that the new detection method can get the detection result of the grounding current fault of the cable, which is more in line with the actual situation and has very high detection accuracy.

Vehicle detection and tracking is a significant part in auxiliary vehicle driving system. Using the traditional detection method based on image information has encountered enormous difficulties, especially in complex background. To solve this problem, a detection method based on deep learning, Faster R-CNN, which has very high detection accuracy and flexibility, is introduced. An algorithm of target tracking with the combination of Camshift and Kalman filter is proposed for vehicle tracking. The computation time of Faster R-CNN cannot achieve realtime detection. We use multi-thread technique to detect and track vehicle by parallel computation for real-time application.

Modern power analysis attacks (PAAs) pose a significant threat to hardware security, and reliably securing integrated systems against advanced PAAs has become an essential design target. The fundamental principle of PAA detection lies in identifying voltage drops induced by malicious probe insertion. However, conventional detection methods often suffer from reduced accuracy when voltage information is obscured by noise. To address this limitation, a real-time PAA detection technique is proposed to achieve high detection accuracy even in environments with significant voltage noise. The voltages of power grid nodes are initially acquired through voltage sensors, and the voltage differential between power grid nodes is evaluated by performing multiple voltage comparisons within a time period, which effectively mitigates the noise effects. Then, these differential measurements are processed by a linear support vector machine (SVM) model to identify anomalous voltage drops. To further optimize hardware efficiency, a reinforcement learning-based method is developed to determine sensor deployment, minimizing power and area overheads while maintaining detection accuracy. Experimental validation of our method demonstrates a high detection accuracy of 87.13% for 0.2 Ω resistance insertions, even under severe noise conditions (20% of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dd</i>).

Machine vision based online detection of PCB defect

Forest fires are severe ecological disasters worldwide that cause extensive ecological destruction and economic losses while threatening biodiversity and human safety. With the escalation of climate change, the frequency and intensity of forest fires are increasing annually, underscoring the urgent need for effective monitoring and early warning systems. This study investigates the application effectiveness of deep learning-based object detection technology in forest fire smoke recognition by using the YOLOv11x algorithm to develop an efficient fire detection model. The objective is to enhance early fire detection capabilities and mitigate potential damage. To improve the model’s applicability and generalizability, two publicly available fire image datasets, WD (Wildfire Dataset) and FFS (Forest Fire Smoke), encompassing various complex scenarios and external conditions, were employed. After 501 training epochs, the model’s detection performance was comprehensively evaluated via multiple metrics, including precision, recall, and mean average precision (mAP50 and mAP50-95). The results demonstrate that YOLOv11x excels in bounding box loss (box loss), classification loss (cls loss), and distribution focal loss (dfl loss), indicating effective optimization of object detection performance across multiple dimensions. Specifically, the model achieved a precision of 0.949, a recall of 0.850, an mAP50 of 0.901, and an mAP50-95 of 0.786, highlighting its high detection accuracy and stability. Analysis of the precision‒recall (PR) curve revealed an average mAP@0.5 of 0.901, further confirming the effectiveness of YOLOv11x in fire smoke detection. Notably, the mAP@0.5 for the smoke category reached 0.962, whereas for the flame category, it was 0.841, indicating superior performance in smoke detection compared with flame detection. This disparity primarily arises from the distinct visual characteristics of flames and smoke; flames possess more vivid colors and defined shapes, facilitating easier recognition by the model, whereas smoke exhibits more ambiguous and variable textures and shapes, increasing detection difficulty. In the test set, 86.89% of the samples had confidence scores exceeding 0.85, further validating the model’s reliability. In summary, the YOLOv11x algorithm demonstrates excellent performance and broad application potential in forest fire smoke recognition, providing robust technical support for early fire warning systems and offering valuable insights for the design of intelligent monitoring systems in related fields.

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

This research aims at developing a real-time simulation-based dynamic traffic model for mixed traffic flow conditions for ATMS/ATIS applications. The real-time model is implemented based on rolling horizon approach. The rolling horizon is advanced for each stage, thus real-time data can be incorporated within the framework. In order to predict normative as well as predictive information, a simulation-based DTA model is employed within each stage. Empirical data for freeways, including flows from toll stations and vehicle detectors, are used to validate the model in a real-time environment.

We envision that in the near future, humanoid robots would share home space and assist us in our daily and routine activities through object manipulations. One of the fundamental technologies that needs to be developed for the robots is to enable them to detect objects and recognize them for effective manipulations and take real-time decisions involving the same. In this paper, we describe a novel architecture that enables multiple low-compute NAO robots to perform real-time detection, recognition and localization of objects in its camera view and take programmable actions based on the detected objects. The proposed algorithm for object detection and localization is an empirical modification of YOLOv3 along with a distributed architecture to operate multiple robots on a central “inference engine”, based on indoor experiments in multiple scenarios, with a smaller weight size and lesser computational requirements. YOLOv3 was chosen after a comparative study of bounding box algorithms was performed with an objective to choose one that strikes the perfect balance among information retention, low inference time and high accuracy for real-time object detection and localization. Quantization of the weights and re-adjusting filter sizes and layer arrangements for convolutions improved the inference time for low-resolution images from the robot's camera feed. The architecture also comprises of an effective end-to-end pipeline to feed the real-time frames from the camera feed to the neural net and use its results for guiding the robot with customizable actions corresponding to the detected class labels.

Real-time object detection and tracking in video streams has become a fundamental component of modern intelligent systems, enabling applications in surveillance, autonomous driving, robotics, healthcare monitoring, and smart cities. The rapid advancement of deep learning techniques has significantly improved the accuracy and efficiency of detecting and tracking multiple objects under complex and dynamic environments. This study presents a deep learning–based framework for real-time object detection and tracking in continuous video streams, emphasizing both computational efficiency and detection robustness.The proposed system integrates a state-of-the-art convolutional neural network (CNN)-based object detector with a reliable multi-object tracking algorithm. The detection module identifies objects of interest in each frame, while the tracking component maintains consistent identities across consecutive frames by utilizing motion prediction and feature association strategies. To achieve real-time performance, the model is optimized through lightweight network architectures and parallel processing on GPU hardware. Data augmentation and transfer learning techniques are employed to enhance generalization and reduce training time. Performance evaluation is conducted on benchmark video datasets using standard metrics such as precision, recall, mean Average Precision (mAP), and tracking accuracy. Experimental results demonstrate that the proposed framework achieves high detection accuracy while maintaining low latency, making it suitable for real-time deployment. Furthermore, the system effectively handles challenges such as occlusion, scale variation, and illumination changes. This research presents a comprehensive study on real-time object detection and tracking in video streams using deep learning techniques. The proposed framework integrates YOLO-based object detection with Deep SORT multi-object tracking to achieve robust spatial localization and temporal identity preservation. The study evaluates performance using benchmark datasets including COCO, MOT17, and KITTI. Mathematical formulations of detection loss, Kalman filtering, and data association are presented. Experimental analysis using precision, recall, mAP, MOTA, ROC-AUC, and PR-AUC demonstrates that the system achieves high detection accuracy while maintaining real-time performance above 35 frames per second. The findings confirm that deep learning-based frameworks provide scalable and efficient solutions for surveillance, autonomous vehicles, and intelligent monitoring systems.

The face feature point detection algorithm has a very broad application prospect in the field of face recognition. In this paper, from the perspective of computer vision, the specific connotation of face feature point detection and face shape indexing with pixel coordinates is elaborated. The overall framework of cascade regression algorithm is built through training and testing, and the face pose changes are extracted based on pose indexing features and weak invariance to ensure that the algorithm can realize real-time face feature point detection. Train all levels of cascade regressors using data distribution statistics to allow the cascade regression framework to complete incremental learning and restore the position of real shape markers. Compare the cascade regression algorithm with existing face feature point detection methods, and verify that the cascade regression algorithm has high detection accuracy and fast detection speed in face feature point detection by pupil localization test and detection speed test. The detection rate of the cascade regression algorithm in the test is more than 90%, the pupil detection accuracy can be controlled at about 3 pixels, and the detection speed is about 87FPS, which is able to quickly and accurately recognize the face feature points, and it has practical and broad application prospects in real life.

Cooperative Adaptive Cruise Control (CACC) is a promising application in autonomous vehicles. In CACC, a platoon is formed, where a leading vehicle sends information to regulate the speed of succeeding following vehicles in the road. In a benign environment, CACC provides tremendous benefits, including reducing fuel consumption, maximizing road capacity, and increasing traffic safety. However, one of the critical security threats in CACC is when a platoon has a compromised leading vehicle, which forges acceleration information sent to the platoon members. Such data falsification attack would lead to traffic instability, high fuel consumption, and potential collisions. As an effort to solve this problem, in this paper, we propose a real-time anomaly detection mechanism using physics laws of kinematics along with data fusion. The proposed technique is applied at each vehicle, where the information received from the leader is validated based on physics laws. To enhance the reliability and support the detection of anomalous behavior, we utilize information sharing in CACC by allowing vehicles and the fixed infrastructure to share sensed information about platoon leaders. In the proposed approach, each vehicle fuses information it receives to reliably detect unexpected deviations. We showed that the proposed approach is effective in detecting acceleration falsification attacks in CACC, and provides high detection accuracy (96%) and negligible false alarm rate. We compare our proposed approach with existing method and showed that the proposed approach provides superior performance in both detection accuracy and execution time.

Intelligent transportation systems (ITS) depend on precise vehicle identification and speed estimation to provide real-time traffic monitoring, accident avoidance, and effective traffic flow management. This paper, a novel deep neural network (DNN)-based method for real-time vehicle detection and speed estimate is presented. For accurate vehicle detection, we use cutting-edge object detection models like YOLOv5, and we use computer vision algorithms to determine a vehicle's speed from video frames. The technology operates reliably in a range of environmental circumstances and processes real-time traffic video streams. High detection accuracy (above 95%) and accurate speed estimation with an average error margin of less than 5 km/h are demonstrated by the experimental findings. A scalable and economical architecture for traffic surveillance applications is provided by the suggested approach.