Faster OreFSDet : A Lightweight and Effective Few-shot Object Detector for Ore Images

• URL: http://arxiv.org/abs/2305.01183v1
• Date: Tue, 2 May 2023 03:30:03 GMT
• Title: Faster OreFSDet : A Lightweight and Effective Few-shot Object Detector for Ore Images
• Authors: Yang Zhang, Le Cheng, Yuting Peng, Chengming Xu, Yanwei Fu, Bo Wu, Guodong Sun
• Abstract summary: General object detection methods often suffer from severe over-fitting with scarce labeled data. We propose a lightweight and effective few-shot detector to achieve competitive performance with general object detection. Our method achieves the smallest model size of 19MB as well as being competitive at 50 FPS detection speed.
• Score: 36.26089993900946
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: For the ore particle size detection, obtaining a sizable amount of high-quality ore labeled data is time-consuming and expensive. General object detection methods often suffer from severe over-fitting with scarce labeled data. Despite their ability to eliminate over-fitting, existing few-shot object detectors encounter drawbacks such as slow detection speed and high memory requirements, making them difficult to implement in a real-world deployment scenario. To this end, we propose a lightweight and effective few-shot detector to achieve competitive performance with general object detection with only a few samples for ore images. First, the proposed support feature mining block characterizes the importance of location information in support features. Next, the relationship guidance block makes full use of support features to guide the generation of accurate candidate proposals. Finally, the dual-scale semantic aggregation module retrieves detailed features at different resolutions to contribute with the prediction process. Experimental results show that our method consistently exceeds the few-shot detectors with an excellent performance gap on all metrics. Moreover, our method achieves the smallest model size of 19MB as well as being competitive at 50 FPS detection speed compared with general object detectors. The source code is available at https://github.com/MVME-HBUT/Faster-OreFSDet.

Related papers

• Practical Video Object Detection via Feature Selection and Aggregation [18.15061460125668]
  Video object detection (VOD) needs to concern the high across-frame variation in object appearance, and the diverse deterioration in some frames. Most of contemporary aggregation methods are tailored for two-stage detectors, suffering from high computational costs. This study invents a very simple yet potent strategy of feature selection and aggregation, gaining significant accuracy at marginal computational expense.
  arXiv  Detail & Related papers  (2024-07-29T02:12:11Z)
• ESOD: Efficient Small Object Detection on High-Resolution Images [36.80623357577051]
  Small objects are usually sparsely distributed and locally clustered. Massive feature extraction computations are wasted on the non-target background area of images. We propose to reuse the detector's backbone to conduct feature-level object-seeking and patch-slicing.
  arXiv  Detail & Related papers  (2024-07-23T12:21:23Z)
• Efficient Meta-Learning Enabled Lightweight Multiscale Few-Shot Object Detection in Remote Sensing Images [15.12889076965307]
  YOLOv7 one-stage detector is subjected to a novel meta-learning training framework. This transformation allows the detector to adeptly address FSOD tasks while capitalizing on its inherent advantage of lightweight. To validate the effectiveness of our proposed detector, we conducted performance comparisons with current state-of-the-art detectors.
  arXiv  Detail & Related papers  (2024-04-29T04:56:52Z)
• Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [70.48554424894728]
  We develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-22T06:19:11Z)
• ASAG: Building Strong One-Decoder-Layer Sparse Detectors via Adaptive Sparse Anchor Generation [50.01244854344167]
  We bridge the performance gap between sparse and dense detectors by proposing Adaptive Sparse Anchor Generator (ASAG) ASAG predicts dynamic anchors on patches rather than grids in a sparse way so that it alleviates the feature conflict problem. Our method outperforms dense-d ones and achieves a better speed-accuracy trade-off.
  arXiv  Detail & Related papers  (2023-08-18T02:06:49Z)
• ETAD: A Unified Framework for Efficient Temporal Action Detection [70.21104995731085]
  Untrimmed video understanding such as temporal action detection (TAD) often suffers from the pain of huge demand for computing resources. We build a unified framework for efficient end-to-end temporal action detection (ETAD) ETAD achieves state-of-the-art performance on both THUMOS-14 and ActivityNet-1.3.
  arXiv  Detail & Related papers  (2022-05-14T21:16:21Z)
• Plug-and-Play Few-shot Object Detection with Meta Strategy and Explicit Localization Inference [78.41932738265345]
  This paper proposes a plug detector that can accurately detect the objects of novel categories without fine-tuning process. We introduce two explicit inferences into the localization process to reduce its dependence on annotated data. It shows a significant lead in both efficiency, precision, and recall under varied evaluation protocols.
  arXiv  Detail & Related papers  (2021-10-26T03:09:57Z)
• Few-shot Object Detection with Feature Attention Highlight Module in Remote Sensing Images [10.92844145381214]
  We propose a few-shot object detector which is designed for detecting novel objects based on only a few examples. Our model is composed of a feature-extractor, a feature attention highlight module as well as a two-stage detection backend. Experiments demonstrate the effectiveness of the proposed method for few-shot cases.
  arXiv  Detail & Related papers  (2020-09-03T12:38:49Z)
• Joint Detection and Tracking in Videos with Identification Features [36.55599286568541]
  We propose the first joint optimization of detection, tracking and re-identification features for videos. Our method reaches the state-of-the-art on MOT, it ranks 1st in the UA-DETRAC'18 tracking challenge among online trackers, and 3rd overall.
  arXiv  Detail & Related papers  (2020-05-21T21:06:40Z)
• EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement [53.69674636044927]
  We present EHSOD, an end-to-end hybrid-supervised object detection system. It can be trained in one shot on both fully and weakly-annotated data. It achieves comparable results on multiple object detection benchmarks with only 30% fully-annotated data.
  arXiv  Detail & Related papers  (2020-02-18T08:04:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.