TL;DR
OrchardDA-YOLO is introduced, a semi-supervised domain adaptation detection framework oriented toward edge deployment that combines a foreground-aware Task-aware Coordinate Attention module with a progressive cross-domain alignment strategy that enhances the model’s feature discriminability for occluded and small-scale targets.
WCAF-YOLO is proposed, a lightweight two-dimensional tomato detector based on a modified YOLOv26n architecture that replaces the P3 backbone downsampling operation with space-to-depth convolution (SPD-Conv) to retain fine-grained spatial information.
The proposed model provides a favorable balance between detection accuracy and computational efficiency, indicating its potential for real-time orchard perception on resource-constrained platforms.
AD-YOLO is presented, a dual-level framework that tackles pseudo-label noise and limited multi-scale adaptability when applied to semi-supervised object detection frameworks from both the detector architecture and the SSOD pipeline.
YOLO-PINE achieves a competitive balance between detection accuracy and efficiency, offering a viable visual perception solution for automated pineapple harvesting on edge devices.
In complex and unstructured orchard environments, accurate fruit detection is essential for yield estimation and robotic harvesting in precision agriculture. However, single-model detectors often suffer from reduced robustness and high miss rates under drastic illumination changes, severe occlusions, and dense fruit ov...
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.
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