Scale-Invariant Feature Disentanglement via Adversarial Learning for UAV-based Object Detection

• URL: http://arxiv.org/abs/2405.15465v2
• Date: Fri, 31 May 2024 08:08:23 GMT
• Title: Scale-Invariant Feature Disentanglement via Adversarial Learning for UAV-based Object Detection
• Authors: Fan Liu, Liang Yao, Chuanyi Zhang, Ting Wu, Xinlei Zhang, Xiruo Jiang, Jun Zhou,
• Abstract summary: We propose to improve single-stage inference accuracy through learning scale-invariant features. We apply our approach to three state-of-the-art lightweight detection frameworks on three benchmark datasets.
• Score: 18.11107031800982
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting objects from Unmanned Aerial Vehicles (UAV) is often hindered by a large number of small objects, resulting in low detection accuracy. To address this issue, mainstream approaches typically utilize multi-stage inferences. Despite their remarkable detecting accuracies, real-time efficiency is sacrificed, making them less practical to handle real applications. To this end, we propose to improve the single-stage inference accuracy through learning scale-invariant features. Specifically, a Scale-Invariant Feature Disentangling module is designed to disentangle scale-related and scale-invariant features. Then an Adversarial Feature Learning scheme is employed to enhance disentanglement. Finally, scale-invariant features are leveraged for robust UAV-based object detection. Furthermore, we construct a multi-modal UAV object detection dataset, State-Air, which incorporates annotated UAV state parameters. We apply our approach to three state-of-the-art lightweight detection frameworks on three benchmark datasets, including State-Air. Extensive experiments demonstrate that our approach can effectively improve model accuracy. Our code and dataset are provided in Supplementary Materials and will be publicly available once the paper is accepted.

Related papers

• Bayesian Detector Combination for Object Detection with Crowdsourced Annotations [49.43709660948812]
  Acquiring fine-grained object detection annotations in unconstrained images is time-consuming, expensive, and prone to noise. We propose a novel Bayesian Detector Combination (BDC) framework to more effectively train object detectors with noisy crowdsourced annotations. BDC is model-agnostic, requires no prior knowledge of the annotators' skill level, and seamlessly integrates with existing object detection models.
  arXiv  Detail & Related papers  (2024-07-10T18:00:54Z)
• Visible and Clear: Finding Tiny Objects in Difference Map [50.54061010335082]
  We introduce a self-reconstruction mechanism in the detection model, and discover the strong correlation between it and the tiny objects. Specifically, we impose a reconstruction head in-between the neck of a detector, constructing a difference map of the reconstructed image and the input, which shows high sensitivity to tiny objects. We further develop a Difference Map Guided Feature Enhancement (DGFE) module to make the tiny feature representation more clear.
  arXiv  Detail & Related papers  (2024-05-18T12:22:26Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images [15.251042369061024]
  Few-shot object detection (FSOD) relies on a large amount of labeled data for training. Scale and orientation variations of objects in remote sensing images pose significant challenges to existing FSOD methods. We propose integrating a feature pyramid network and utilizing prototype features to enhance query features.
  arXiv  Detail & Related papers  (2023-03-13T02:21:38Z)
• Generalized Few-Shot 3D Object Detection of LiDAR Point Cloud for Autonomous Driving [91.39625612027386]
  We propose a novel task, called generalized few-shot 3D object detection, where we have a large amount of training data for common (base) objects, but only a few data for rare (novel) classes. Specifically, we analyze in-depth differences between images and point clouds, and then present a practical principle for the few-shot setting in the 3D LiDAR dataset. To solve this task, we propose an incremental fine-tuning method to extend existing 3D detection models to recognize both common and rare objects.
  arXiv  Detail & Related papers  (2023-02-08T07:11:36Z)
• Scaling Novel Object Detection with Weakly Supervised Detection Transformers [21.219817483091166]
  We propose the Weakly Supervised Detection Transformer, which enables efficient knowledge transfer from a large-scale pretraining dataset to WSOD finetuning. Our experiments show that our approach outperforms previous state-of-the-art models on large-scale novel object detection datasets.
  arXiv  Detail & Related papers  (2022-07-11T21:45:54Z)
• Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving [45.405303803618]
  We investigate diversity-based active learning (AL) as a potential solution to alleviate the annotation burden. We propose a novel acquisition function that enforces spatial and temporal diversity in the selected samples. We demonstrate the effectiveness of the proposed method on the nuScenes dataset and show that it outperforms existing AL strategies significantly.
  arXiv  Detail & Related papers  (2022-05-16T14:21:30Z)
• Embracing Single Stride 3D Object Detector with Sparse Transformer [63.179720817019096]
  In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases. Many 3D detectors directly follow the common practice of 2D detectors, which downsample the feature maps even after quantizing the point clouds. We propose Single-stride Sparse Transformer (SST) to maintain the original resolution from the beginning to the end of the network.
  arXiv  Detail & Related papers  (2021-12-13T02:12:02Z)
• Pattern-Aware Data Augmentation for LiDAR 3D Object Detection [7.394029879643516]
  We propose pattern-aware ground truth sampling, a data augmentation technique that downsamples an object's point cloud based on the LiDAR's characteristics. We improve the performance of PV-RCNN on the car class by more than 0.7 percent on the KITTI validation split at distances greater than 25 m.
  arXiv  Detail & Related papers  (2021-11-30T19:14:47Z)
• Robust and Accurate Object Detection via Adversarial Learning [111.36192453882195]
  This work augments the fine-tuning stage for object detectors by exploring adversarial examples. Our approach boosts the performance of state-of-the-art EfficientDets by +1.1 mAP on the object detection benchmark.
  arXiv  Detail & Related papers  (2021-03-23T19:45:26Z)
• Towards Better Object Detection in Scale Variation with Adaptive Feature Selection [3.5352273012717044]
  We propose a novel adaptive feature selection module (AFSM) to automatically learn the way to fuse multi-level representations in the channel dimension. It significantly improves the performance of the detectors that have a feature pyramid structure. A class-aware sampling mechanism (CASM) is proposed to tackle the class imbalance problem.
  arXiv  Detail & Related papers  (2020-12-06T13:41:20Z)

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.