AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
HA-DETR performs differentiated processing and adaptive enhancement on features across semantic levels to improve representational capacity and demonstrate HA-DETR’s superiority on DUO and UTDAC2020 datasets.
A novel underwater target detection framework that integrates feature enhancement with semantic-spatial guided fusion, built upon the RT-DETR architecture, that significantly reduces false positives and missed detections while maintaining real-time performance is proposed.
Marine organisms exhibit substantial variation in appearance and shape across diverse underwater environments and are often characterized by considerably smaller object scales than their terrestrial counterparts. These properties pose significant challenges to generic object detectors, particularly in capturing shape-v...
Experimental results demonstrate that the proposed Bidirectional weighted Concat with Efficient multi-scale Attention You Only Look Once (BCEA-YOLO) method outperforms competing methods for underwater object detection, and edge deployment tests on the NVIDIA Jetson platform validate its real-time inference efficiency.
Experimental results on the VisDrone2019 dataset demonstrate that Aerial-YOLO significantly improves small object detection performance while maintaining real-time inference capability, and strong generalization ability and robustness of the proposed method are verified.
Underwater object detection (UOD) in unknown underwater environments remains challenging due to domain-dependent image degradation, which causes unstable feature representations and substantially reduces cross-domain generalization. To address this problem, we propose MFAFNet, an end-to-end Multiscale Frequency-Adaptiv...
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.
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