A Study on Comparative Analysis of Object Detection-based Real-Time Wildfire and Class B Fire Detection Performance

Kısırlık son 50 yılda dünya çapında önemli bir sağlık problemi haline gelmiştir. Dünyanın farklı bölgelerine göre oranı değişen bu problem ortalama olarak dünyadaki her on çiften birini etkilemektedir. Erkek kaynaklı kısırlık teşhisi, sperm kalitesi değerlendirilerek yapılır. Sperm kalitesi araştırılırken sperm sayısı, hareketi ve morfolojik yapısı değerlendirilir. Sperm hareketi ve sayımının analizi öncesinde sperm tespiti önemli bir adımdır. Bu çalışmada, oluşturulan özgün yeni semen video veri kümesi üzerinde Faster R-CNN ve YOLOv3 derin öğrenme yöntemleri kullanılarak otonom sperm tespiti gerçekleştirilmiştir. Çalışma kapsamında oluşturulan bu özgün veri kümesi, mikroskop altında semen örneklerinin cep telefonu yardımı ile elde edilen 10 hastaya ait semen videolarını ve içeriğindeki nesnelerin sperm ve sperm olmayan şeklindeki etiket bilgisini içermektedir. Analiz için hazırlanmış etiketli videolar hasta odaklı ve hasta bağımsız olmak üzere iki senaryo ile değerlendirilmiştir. İlk senaryomuzda etiketli sekiz video birleştirilerek Faster R-CNN ve YOLOv3 modelleri 3 farklı oranda oluşturulmuş veriler ile eğitilmiş ve test edilmiştir. İkinci senaryoda ise eğitilmiş her bir modelimiz daha önce eğitime hiç katılmamış iki video ile test edilmiştir. İkinci senaryomuzda, eğitimine hiç katılmamış videolar kullanılarak algılama performansları değerlendirilmiştir. Yapılan çalışmada YOLOv3 modeli ile bireysel videolarda %96, ortalama da ise %84,5 gibi mAP sperm tespit sonuçları elde edilmiştir. Sonuçlar nesne tespitinin doğruluğu ve eğitim süreleri gibi iki önemli kriter ile karşılaştırıldığında YOLOv3 yöntemi Faster R-CNN yönteminden daha başarılı olduğu gözlemlenmiştir.

Intelligent detection is vital for achieving the intelligent picking operation of daylily, but complex field environments pose challenges due to branch occlusion, overlapping plants, and uneven lighting. To address these challenges, this study selected an intelligent detection model based on YOLOv5s for daylily, the depth and width parameters of the YOLOv5s network were optimized, with Ghost, Transformer, and MobileNetv3 lightweight networks used to optimize the CSPDarknet backbone network of YOLOv5s, continuously improving the model's performance. The experimental results show that the original YOLOv5s model increased mean average precision (mAP) by 49%, 44%, and 24.9% compared to YOLOv4, SSD, and Faster R-CNN models, optimizing the depth and width parameters of the network increased the mAP of the original YOLOv5s model by 7.7%, and the YOLOv5s model with Transformer as the backbone network increased the mAP by 0.2% and the inference speed by 69% compared to the model after network parameter optimization. The optimized YOLOv5s model provided precision, recall rate, mAP, and inference speed of 81.4%, 74.4%, 78.1%, and 93 frames per second (FPS), which can achieve accurate and fast detection of daylily in complex field environments. The research results can provide data and experimental references for developing intelligent picking equipment for daylily.

This paper proposes a complex overlapping pedestrian target detection model based on yolov3 model by multi-scale feature fusion and context-aware mechanism. The SONY A7R3a camera shot the model on campus, and the data set was obtained after editing and collating. There were 358 high-definition videos with a resolution of 1920*1080, and the frame rate was 50HZ, about 179,000 frames. Through testing, this paper finds that compared with Single Shot Multibox Detector (SSD), the detection accuracy of the newly proposed model is slightly improved, the detection accuracy is the same as that of Faster R-CNN, and the detection accuracy of the newly proposed model is slightly worse than that of RetinaNet. However, the detection speed of Yolov3 is more than twice that of Single Shot Multibox Detector, RetinaNet and Faster R-CNN. The input size of Yolov3 is 320*320, and the processing of a single image only needs 22ms, so the detection speed of the simplified Yolov3 tiny is faster.

Object detection is a cornerstone task in computer vision, impacting diverse domains from autonomous systems to medical imaging. This study presents a systematic evaluation of three leading detection architecturesFaster Region-based Convolutional Neural Networks (Faster R-CNN), You Only Look Once version 8 (YOLOv8), and Detection Transformer (DETR), with the objective of evaluating their performance characteristics and practical deployment potential. Through comparative experiments, the research evaluates Faster R-CNNs precision (Delivers 76.4% mean Average Precision with a processing speed of 5-7 Frames Per Second (FPS)), YOLOv8s real-time efficiency (53.9% mean Average Precision (mAP) at 80+ FPS), and DETR attention-based innovation (42% AP). Results highlight the strengths and weaknesses of each model: Faster R-CNN excels in accuracy-demanding applications like medical diagnosis, YOLOv8 dominates real-time tasks such as autonomous driving, and DETR offers promising temporal analysis capabilities despite higher computational costs. The study proposes innovative solutions, including cross-scale attention mechanisms and dynamic inference techniques, to address limitations such as small-object detection and edge deployment. The findings provide valuable insights for architecture selection, offering actionable guidelines for industrial implementation and laying the groundwork for future advancements in multimodal fusion and self-supervised learning paradigms. This framework accelerates the development of next-generation detection systems tailored for emerging Artificial Intelligence (AI) applications.

Improving the detection accuracy of hyperbola in B-scan images has been a considerable challenge for ground penetrating radar (GPR) to detect tree roots. In this paper, a method for data enhancement and target detection, both based on deep learning was proposed to identify hyperbolas in GPR B-scan images. First, the authors used a cyclic consistent adversarial network (CycleGAN) to augment the original data. In this procedure, the hyperbolic features of the images were preserved and created a wider variety of training samples. Then, the authors could apply the enhanced dataset to the YOLOv5 detection model to evaluate the effectiveness of their method. Meanwhile, the detection effects of Yolov3, Yolov5, Faster R-CNN, and CenterNet detection models on the enhanced dataset were compared. The results showed that applying the enhanced dataset to the Yolov5 detection model exhibited better detection accuracy compared to other combinations of datasets and detection models. The authors demonstrate that the proposed method increases data diversity and the number of samples, improving the precision and recall of hyperbolic curves. These results provide a new method for tree root localization with important effects.

Detecting wildfires and smoke is essential for safeguarding forest ecosystems and offers critical information for the early evaluation and prevention of such incidents. The advancement of unmanned aerial vehicle (UAV) remote sensing has further enhanced the detection of wildfires and smoke, which enables rapid and accurate identification. This paper presents an integrated one-stage object detection framework designed for the simultaneous identification of wildfires and smoke in UAV imagery. By leveraging mixed data augmentation techniques, the framework enriches the dataset with small targets to enhance its detection performance for small wildfires and smoke targets. A novel backbone enhancement strategy, integrating region convolution and feature refinement modules, is developed to facilitate the ability to localize smoke features with high transparency within complex backgrounds. By integrating the shape aware loss function, the proposed framework enables the effective capture of irregularly shaped smoke and fire targets with complex edges, facilitating the accurate identification and localization of wildfires and smoke. Experiments conducted on a UAV remote sensing dataset demonstrate that the proposed framework achieves a promising detection performance in terms of both accuracy and speed. The proposed framework attains a mean Average Precision (mAP) of 79.28%, an F1 score of 76.14%, and a processing speed of 8.98 frames per second (FPS). These results reflect increases of 4.27%, 1.96%, and 0.16 FPS compared to the YOLOv10 model. Ablation studies further validate that the incorporation of mixed data augmentation, feature refinement models, and shape aware loss results in substantial improvements over the YOLOv10 model. The findings highlight the framework’s capability to rapidly and effectively identify wildfires and smoke using UAV imagery, thereby providing a valuable foundation for proactive forest fire prevention measures.

Early fire detection minimizes casualties and property damage. This paper presents a novel fire detection framework built upon YOLOv12 architecture with Content-Adaptive Area Attention (CAAA) and Cross-Scale Consistent Attention Fusion (CCAF). These mechanisms address limitations in dynamic scene adaptation and multi-scale feature consistency. Our approach achieves mAP@50 of 78.6% and mAP@50-95 of 46.9% while maintaining 57.6 FPS inference speed. The system demonstrates robustness across diverse conditions including varying lighting and weather. Ablation studies show CAAA provides 2.1% mAP improvement and CCAF contributes 1.4% enhancement. Comprehensive comparisons with mainstream detectors including YOLOv11, Faster R-CNN, and DETR demonstrate superior accuracy-efficiency trade-offs. This research bridges theoretical advancements with practical fire safety applications for smart cities and forest fire prevention.

With the rapid development of computer vision and machine vision, methods based on deep learning have achieved good results in the field of object detection, identification, and tracking. However, for the detection and identification of rebars in smart construction sites, it is very difficult to perform accurate real-time detection of rebars by using object detection technology on the equipment in the field because of the problems of the dense cross-section between bundled bars, the mutual adhesion of cross-section boundaries, and mutual occlusion between cross-sections. To address the above problems, we propose a multi-scale rebar detection network RebarNet with an embedded attention mechanism based on YOLOv5, combining the K-means++ algorithm, attention mechanism, a newly designed SD_IoU Loss, and multi-scale feature fusion, aiming to solve the problems of missed and false detection in dense small object detection. Due to the problems of scarce rebar cross-section datasets, no publicly available large datasets, and weak rebar cross-sectional features, we constructed a new rebar cross-sectional dataset, used a semi-automatic annotation method to annotate part of the dataset, and then used the data enhancement algorithm to expand the rebar dataset. The experimental results show that the average accuracy (mAP) of our proposed RebarNet network is 97.9%, which is comparable to mainstream target detection algorithms such as Faster R-CNN, SSD, RetinaNet, CenterNet, CornerNet, YOLOv3, YOLOv4, and YOLOv5s. mAP0.5 is improved by 8.1%, 13%, 26.4%, 25.8%, 26.2%, 11.7%, 7.6%, and 9%, respectively. In addition, the frames per second (FPS) transmission reaches 89 frames per second, the model weight is only 17.0 MB. In summary, the proposed RebarNet can effectively reduce missed and false detections in the rebar counting detection task based on real-time detection.

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

<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.

Region NMS-based deep network for gigapixel level pedestrian detection with two-step cropping

Recent advances in Artificial Intelligence (AI) and Computer Vision have significantly enhanced the potential of Advanced Driver Assistance Systems (ADAS). However, existing solutions remain limited by high computational cost, single-function design, and dependence on expensive sensors such as radar and LiDAR. This study presents DriveRight, an embedded AI-based driver-assistance system that integrates multi-scenario hazard detection and real-time object detection and alerting using a single low-cost vision sensor on a Raspberry Pi platform. The system leverages a simulation-to-deployment pipeline, combining CARLA-based synthetic training environments with TensorFlow deep learning models, including SSD Inception v2, MobileNet-SSD, and Faster R-CNN. Experimental results show that Faster R-CNN achieved 92.1% detection accuracy for vehicles and 90.3% for traffic signs, while MobileNet-SSD achieved real-time performance at 14.6 frames per second (FPS) with minimal latency of 2.8 seconds on embedded hardware. Field tests validated the system’s ability to accurately detect and classify stop signs, vehicles, and lane deviations under varying lighting and motion conditions, triggering timely alerts to the driver. The prototype demonstrates a cost-effective and energy-efficient AI solution (&lt; 12 W) for intelligent transportation systems. The findings establish the feasibility of deploying IoT-based ADAS and deep learning–driven driver-assistance technologies in low-cost, sustainable embedded platforms, bridging the gap between research-grade ADAS and practical real-world deployment.

With the rapid development of Internet of Things (IoT) and UAV technology, for the whole IoT system of vehicle detection, the middle and high level of information transmission and server processing has made a breakthrough, so at the bottom of the real-time detection of the vehicle by the UAV is the key to the whole system. However, UAV vehicle detection faces the challenges of too many small targets in the image leading to low detection accuracy, limited hardware platform resources requiring control of model size, and real-time detection requiring high inference speed. Aiming at the above problems, we propose a lightweight model PrFu-YOLO based on YOLOv8 improvement, which achieves a good balance between the accuracy, inference speed, and model size. And it realizes real-time vehicle detection embedded in an UAV platform. To solve the problem of low vehicle detection accuracy, we design a new structure PrFuFPN based on adding a small target detection layer to achieve more advanced feature fusion. To address the limited resources of the platform and the problem of real-time vehicle detection, we add GhostConv to the structure and constantly try to adjust the parameters of the network. Finally, extensive experiments were conducted on the VisDrone2019 and CARPK data sets to fully evaluate the model. Compared to YOLOv8s on the VisDrone2019 test set, mAP50 was improved by 10.05%, mA95 by 14.54%, the number of parameters was reduced by 8.13%, and the model size was reduced by 12.5%, while the FPS of 67.13 fully met the needs of real-time detection.

Current indoor fire detection systems often have limitations, including insufficient intelligence and slow response times. Recent studies have started to explore AI vision-based methods for real-time fire and smoke detection to replace traditional fire sensors. However, indoor environments present many interference factors, and few methods can effectively demonstrate superior fire and smoke detection performance under multiple simulated interference conditions. To meet the requirements of real-time, high accuracy and anti-interference, a 3D convolutional neural network (3D-CNN)-based real-time fire and smoke detection model has been developed via red-green-blue (RGB) and near-infrared (NIR) feature fusion. RGB and NIR images with complementary information were used as dual-stream inputs to the model. The feature extraction module was enhanced by 3D attention mechanisms to simultaneously capture dynamic features between frames and static features within frames of fire and smoke. A non-linear feature gated fusion module has been developed to solve the challenges of missing multi-modal features and insufficient fusion. To validate the engineering effectiveness of the model, a large-scale multi-modal dataset was constructed in real indoor building scenarios, and a real-time indoor fire and smoke detection system was also developed and deployed at the experimental site.

Target detection in wide-band SAR image faces problems with speckle noise and small target detection. This paper uses the DiffusionDet model to solve the problem. DiffusionDet transforms target detection into a denoising diffusion process from noise box to target box, which consists of an image encoder and a detection decoder. When implemented, the pre-trained image encoder is first used to extract SAR image features. Then, the diffusion model is applied to generate candidate positions for target detection boxes on the feature map. By adding noise to the true value box and learning the inversion process, the target box is gradually refined. In the training phase, a SAR image dataset with true value annotations is used for training. Finally, the optimized DiffusionDet model is applied to the wide-band SAR image target detection task, and the detection performance is improved by adjusting parameters. The maximum IoU value of the DiffusionDet model reaches 1, and the average detection time is only 289.325 milliseconds, which is far lower than Faster R-CNN's <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5 3 4. 1 2 5}$</tex> milliseconds. In addition, the ablation experiment also reveals the trade-off between the number of sampling times and the number of iterations on detection accuracy and inference speed. The DiffusionDet model effectively solves the problem of speckle noise and small target detection in wide-band SAR image target detection, and provides a new method for SAR image target detection.