AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work introduces a Tri-Directional Attention module into the backbone of the YOLOv11 baseline framework, modeling the spatial position information via a three-way attention mechanism to capture richer fine-grained features and devise a Cross-Dimension Feature Interaction module to enhance the recognition accuracy for non-salient objects.
Enhanced Feature-Aware YOLO (EFA-YOLO), a lightweight detection framework based on YOLO11n, achieves a favorable balance between detection accuracy and computational efficiency, demonstrating real-time performance on the evaluated GPU platform and indicating potential for further edge-oriented optimization.
UAV imagery is particularly difficult for object detection because targets occupy few pixels, occur in dense or occluded groups, and must be separated from extensive background texture. Adding a high-resolution detection layer improves the visibility of tiny instances, but it also carries road markings, building contou...
Small-object detection in remote-sensing imagery remains challenging because targets often occupy only a few pixels and are further affected by substantial scale variations, dense spatial distributions, and complex backgrounds. To address these issues, this study develops MFP-YOLO, a lightweight detector based on YOL...
As a fundamental image processing task, object Re-Identification (ReID) aims to retrieve objects across non-overlapping cameras. Recently, with the development of deep learning, significant advancements have been made in object ReID. However, most existing methods suffer from generalization due to the limited size and...
Tests on the COCO dataset show that EnYOLO-World offers a better trade-off between accuracy and speed compared to existing real-time detectors, demonstrating the value of optimizing both backbone feature retention and neck-level fusion strategies.
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...
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