To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports,this paper proposes an enhanced MBS-YOLO(Multi-Branch Small Target...To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports,this paper proposes an enhanced MBS-YOLO(Multi-Branch Small Target Detection YOLO)model for anti-drone object detection,based on the YOLOv8 architecture.To overcome the limitations of existing methods in detecting small objects within complex backgrounds,we designed a C2f-Pu module with excellent feature extraction capability and a more compact parameter set,aiming to reduce the model’s computational complexity.To improve multi-scale feature fusion,we construct a Multi-Branch Feature Pyramid Network(MB-FPN)that employs a cross-level feature fusion strategy to enhance the model’s representation of small objects.Additionally,a shared detail-enhanced detection head is introduced to address the large size variations of Unmanned Aerial Vehicle(UAV)targets,thereby improving detection performance across different scales.Experimental results demonstrate that the proposed model achieves consistent improvements across multiple benchmarks.On the Det-Fly dataset,it improves precision by 3%,recall by 5.6%,and mAP50 by 4.5%compared with the baseline,while reducing parameters by 21.2%.Cross-validation on the VisDrone dataset further validates its robustness,yielding additional gains of 3.2%in precision,6.1%in recall,and 4.8%in mAP50 over the original YOLOv8.These findings confirm the effectiveness of the proposed algorithm in enhancing UAV detection performance under complex scenarios.展开更多
Classroom behavior recognition is a hot research topic,which plays a vital role in assessing and improving the quality of classroom teaching.However,existing classroom behavior recognition methods have challenges for ...Classroom behavior recognition is a hot research topic,which plays a vital role in assessing and improving the quality of classroom teaching.However,existing classroom behavior recognition methods have challenges for high recognition accuracy with datasets with problems such as scenes with blurred pictures,and inconsistent objects.To address this challenge,we proposed an effective,lightweight object detector method called the RFNet model(YOLO-FR).The YOLO-FR is a lightweight and effective model.Specifically,for efficient multi-scale feature extraction,effective feature pyramid shared convolutional(FPSC)was designed to improve the feature extract performance by leveraging convolutional layers with varying dilation rates from the input image in the backbone.Secondly,to address the problem of multi-scale variability in the scene,we design the Rep Ghost fusion Cross Stage Partial and Efficient Layer Aggregation Network(RGCSPELAN)to improve the network performance further and reduce the amount of computation and the number of parameters.In addition,by conducting experimental valuation on the SCB dataset3 and STBD-08 dataset.Experimental results indicate that,compared to the baseline model,the RFNet model has increased mean accuracy precision(mAP@50)from 69.6%to 71.0%on the SCB dataset3 and from 91.8%to 93.1%on the STBD-08 dataset.The RFNet approach has effectiveness precision at 68.6%,surpassing the baseline method(YOLOv11)at 3.3%and archieve the minimal size(4.9 M)on the SCB dataset3.Finally,comparing it with other algorithms,it accurately detects student behavior in complex classroom environments results confirmed that RFNet is well-suited for real-time and efficiently recognizing classroom behaviors.展开更多
基金supported by the Key R&D Programof Xianyang City,Shaanxi Province(L2024-ZDYF-ZDYF-GY-0043).
文摘To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports,this paper proposes an enhanced MBS-YOLO(Multi-Branch Small Target Detection YOLO)model for anti-drone object detection,based on the YOLOv8 architecture.To overcome the limitations of existing methods in detecting small objects within complex backgrounds,we designed a C2f-Pu module with excellent feature extraction capability and a more compact parameter set,aiming to reduce the model’s computational complexity.To improve multi-scale feature fusion,we construct a Multi-Branch Feature Pyramid Network(MB-FPN)that employs a cross-level feature fusion strategy to enhance the model’s representation of small objects.Additionally,a shared detail-enhanced detection head is introduced to address the large size variations of Unmanned Aerial Vehicle(UAV)targets,thereby improving detection performance across different scales.Experimental results demonstrate that the proposed model achieves consistent improvements across multiple benchmarks.On the Det-Fly dataset,it improves precision by 3%,recall by 5.6%,and mAP50 by 4.5%compared with the baseline,while reducing parameters by 21.2%.Cross-validation on the VisDrone dataset further validates its robustness,yielding additional gains of 3.2%in precision,6.1%in recall,and 4.8%in mAP50 over the original YOLOv8.These findings confirm the effectiveness of the proposed algorithm in enhancing UAV detection performance under complex scenarios.
基金suported by the Fundamental Research Grant Scheme(FRGS)of Universiti Sains Malaysia,Research Number:FRGS/1/2024/ICT02/USM/02/1.
文摘Classroom behavior recognition is a hot research topic,which plays a vital role in assessing and improving the quality of classroom teaching.However,existing classroom behavior recognition methods have challenges for high recognition accuracy with datasets with problems such as scenes with blurred pictures,and inconsistent objects.To address this challenge,we proposed an effective,lightweight object detector method called the RFNet model(YOLO-FR).The YOLO-FR is a lightweight and effective model.Specifically,for efficient multi-scale feature extraction,effective feature pyramid shared convolutional(FPSC)was designed to improve the feature extract performance by leveraging convolutional layers with varying dilation rates from the input image in the backbone.Secondly,to address the problem of multi-scale variability in the scene,we design the Rep Ghost fusion Cross Stage Partial and Efficient Layer Aggregation Network(RGCSPELAN)to improve the network performance further and reduce the amount of computation and the number of parameters.In addition,by conducting experimental valuation on the SCB dataset3 and STBD-08 dataset.Experimental results indicate that,compared to the baseline model,the RFNet model has increased mean accuracy precision(mAP@50)from 69.6%to 71.0%on the SCB dataset3 and from 91.8%to 93.1%on the STBD-08 dataset.The RFNet approach has effectiveness precision at 68.6%,surpassing the baseline method(YOLOv11)at 3.3%and archieve the minimal size(4.9 M)on the SCB dataset3.Finally,comparing it with other algorithms,it accurately detects student behavior in complex classroom environments results confirmed that RFNet is well-suited for real-time and efficiently recognizing classroom behaviors.
