TL;DR
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.
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 re...
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.
This work proposesours, an aerial-image detector built on the YOLO12 architecture, which combines a triple-path high-frequency enhancement convolution module (TriPathHFConv), receptive-field coordinate-attention convolution (RFCAConv), and a Mamba-based global-context module.
MFRA-YOLOv11, an enhanced YOLOv11s-based network for remote sensing small object detection under the horizontal bounding box paradigm, which integrates multiscale feature extraction and object region awareness to improve detection accuracy.
An improved YOLOv8 lightweight detail dynamic fusion algorithm (LDDF-YOLO) is proposed, to remedy the insufficient adaptation capability of vanilla lightweight YOLOv8 in remote sensing object detection tasks and possesses robust detection performance in challenging remote sensing scenarios with dense small targets and...
SSM-YOLO11s is proposed, a lightweight model optimized for small object detection in aerial imagery that achieves a superior balance between precision and efficiency compared to state-of-the-art models.
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