Selected work

Representative Papers

Boundary and Position Information Mining for Aerial Small Object Detection

Jan 23, 2026

This work addresses the challenge of detecting small objects in drone-captured aerial images, which suffer from significant scale variation and blurred boundaries. To tackle this issue, the authors propose a Boundary and Position Information Mining (BPIM) framework that integrates Position Information Guidance (PIG), Boundary Information Guidance (BIG), Cross-Scale Fusion (CSF), and Triple Feature Fusion (TFF), complemented by an Adaptive Weight Fusion (AWF) mechanism. This design effectively enhances contextual awareness and discriminative capability for small objects. Evaluated on the VisDrone2021, DOTA1.0, and WiderPerson benchmarks, BPIM substantially outperforms the YOLOv5-P2 baseline, achieving state-of-the-art performance while maintaining manageable computational overhead.

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Enhancing Traffic Sign Recognition On The Performance Based On Yolov8

Apr 02, 2025

To address low detection and recognition accuracy of traffic signs under complex conditions—including small objects, occlusion, illumination variations, and class imbalance—this paper proposes a lightweight and efficient YOLOv8-based framework. The method innovatively integrates Coordinate Attention (CA), BiFPN for multi-scale feature fusion, ODConv for dynamic convolution, and LSKA for large-kernel selective attention, jointly optimized with EIoU/WIoU loss functions and Focal Loss to enhance classification discrimination. Evaluated on GTSRB, TT100K, and GTSDB benchmarks, the approach achieves consistent mAP improvements of 3.2–5.7% over baseline models, significantly boosting robustness in adverse scenarios while maintaining real-time inference capability on edge devices. This work delivers a high-accuracy, low-latency traffic sign perception solution tailored for autonomous driving and ADAS applications.

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Recent publications

Latest Papers

Boundary and Position Information Mining for Aerial Small Object Detection

Jan 23, 2026

This work addresses the challenge of detecting small objects in drone-captured aerial images, which suffer from significant scale variation and blurred boundaries. To tackle this issue, the authors propose a Boundary and Position Information Mining (BPIM) framework that integrates Position Information Guidance (PIG), Boundary Information Guidance (BIG), Cross-Scale Fusion (CSF), and Triple Feature Fusion (TFF), complemented by an Adaptive Weight Fusion (AWF) mechanism. This design effectively enhances contextual awareness and discriminative capability for small objects. Evaluated on the VisDrone2021, DOTA1.0, and WiderPerson benchmarks, BPIM substantially outperforms the YOLOv5-P2 baseline, achieving state-of-the-art performance while maintaining manageable computational overhead.

0 citationsRead paper

Enhancing Traffic Sign Recognition On The Performance Based On Yolov8

Apr 02, 2025

To address low detection and recognition accuracy of traffic signs under complex conditions—including small objects, occlusion, illumination variations, and class imbalance—this paper proposes a lightweight and efficient YOLOv8-based framework. The method innovatively integrates Coordinate Attention (CA), BiFPN for multi-scale feature fusion, ODConv for dynamic convolution, and LSKA for large-kernel selective attention, jointly optimized with EIoU/WIoU loss functions and Focal Loss to enhance classification discrimination. Evaluated on GTSRB, TT100K, and GTSDB benchmarks, the approach achieves consistent mAP improvements of 3.2–5.7% over baseline models, significantly boosting robustness in adverse scenarios while maintaining real-time inference capability on edge devices. This work delivers a high-accuracy, low-latency traffic sign perception solution tailored for autonomous driving and ADAS applications.

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