Object Detection by Attention-Guided Feature Fusion Network
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Summary
Attention-guided Feature Fusion Network method is proposed, which is capable of reliably detecting objects of a wider range of sizes, outperforms previous methods with an mAP value of 85.3% and achieves an excellent result in helmet detection.
- Type
- article
- Published
- 2022-04-26
- Cited by
- 7
- References
- 14
- Access
- Open access
- OpenAlex
- https://openalex.org/W4224951256
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:249242204
Keywords
Computer science, Feature (linguistics), Object detection, Artificial intelligence, Object (grammar)
References
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- The Pascal Visual Object Classes (VOC) Challenge
- ImageNet classification with deep convolutional neural networks
- DSSD : Deconvolutional Single Shot Detector
- YOLOv3: An Incremental Improvement
- Deep Learning for Generic Object Detection: A Survey
- Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3
- Correction to: Single Image Super-Resolution via a Holistic Attention Network
- A FACTORIAL ANALYSIS OF INDUSTRIAL SAFETY
- SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks
- BAM: Bottleneck Attention Module
- SSD: Single Shot MultiBox Detector
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Adam: A Method for Stochastic Optimization
- International Journal of Computer Vision manuscript No. (will be inserted by the editor) The PASCAL Visual Object Classes (VOC) Challenge
Cited by
- MCANet: multi-scale contextual feature fusion network based on Atrous convolution
- Knee Injury Diagnosis with Data and Feature Fusion-Enhanced Multi-Label Classification Network
- Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar
- Research and Optimization of Electrode-Assisted Human Key Point Detection Algorithm Based on Channel Attention and Adaptive Feature Fusion
- BFANet: Bidirectional feature aggregation network for efficient and accurate object detection
- Few-Shot Object Detection for Remote Sensing Imagery Using Segmentation Assistance and Triplet Head
- Spatial Attention-Based Convoluted Gazelle Neural Network (SACGNN): Advanced Clutter Removal Across Diverse Surfaces
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