TL;DR
A lightweight object detection framework, termed MDCF-YOLO, which achieves a superior accuracy-efficiency trade-off compared to state-of-the-art lightweight object detectors and exhibits similarly competitive performance on the AI-TOD dataset, further validating its effectiveness and generalization capability in UAV remote sensing scenarios.
A high-level feature channel compression strategy is proposed, which compresses redundant high-level channels and removes the P5 detection head, thereby reducing model complexity while preserving key semantic information in a lightweight detection framework named QS-YOLOv8.
The proposed Multi-scale Attention Enhancement Feature Fusion Network (MAEF-Net) systematically optimizes the overall detection pipeline from three aspects, namely feature enhancement, feature fusion, and prediction refinement, and achieves a favorable balance between detection accuracy and computational efficiency whi...
An Adaptive and Scalable YOLO model named AS-YOLOR (Adaptive and Scalable YOLO for Rotated object detection), based on the YOLOv8 baseline is proposed, providing a solution with strong practical potential for achieving efficient and high-precision detection of small, rotated objects.
Extensive experiments on VisDrone2019, UAVDT2018 and AI-TOD2021 demonstrate that PHAF-YOLO achieves a superior accuracy-efficiency trade-off across all model scales and outperforms mainstream real-time detectors.
A layered enhancement network named LE-YOLO is proposed, which initiates enhancement at the feature level by integrating the DySample module, which replaces standard upsampling operators through a dynamic point sampling mechanism, and significantly improves the reconstruction of fine-grained details with negligible com...
Improvements over the baseline YOLOv8 are presented, validating the effectiveness and practical value of the proposed method for small-object detection in remote sensing scenarios.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
