Dynamic Tiling: A Model-Agnostic, Adaptive, Scalable, and Inference-Data-Centric Approach for Efficient and Accurate Small Object Detection

• URL: http://arxiv.org/abs/2309.11069v1
• Date: Wed, 20 Sep 2023 05:25:12 GMT
• Title: Dynamic Tiling: A Model-Agnostic, Adaptive, Scalable, and Inference-Data-Centric Approach for Efficient and Accurate Small Object Detection
• Authors: Son The Nguyen, Theja Tulabandhula, Duy Nguyen
• Abstract summary: Dynamic Tiling is a model-agnostic, adaptive, and scalable approach for small object detection. Our method effectively resolves fragmented objects, improves detection accuracy, and minimizes computational overhead. Overall, Dynamic Tiling outperforms existing model-agnostic uniform cropping methods.
• Score: 3.8332251841430423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Dynamic Tiling, a model-agnostic, adaptive, and scalable approach for small object detection, anchored in our inference-data-centric philosophy. Dynamic Tiling starts with non-overlapping tiles for initial detections and utilizes dynamic overlapping rates along with a tile minimizer. This dual approach effectively resolves fragmented objects, improves detection accuracy, and minimizes computational overhead by reducing the number of forward passes through the object detection model. Adaptable to a variety of operational environments, our method negates the need for laborious recalibration. Additionally, our large-small filtering mechanism boosts the detection quality across a range of object sizes. Overall, Dynamic Tiling outperforms existing model-agnostic uniform cropping methods, setting new benchmarks for efficiency and accuracy.

Related papers

• DASSF: Dynamic-Attention Scale-Sequence Fusion for Aerial Object Detection [6.635903943457569]
  The original YOLO algorithm has low overall detection accuracy due to its weak ability to perceive targets of different scales. This paper proposes a dynamic-attention scale-sequence fusion algorithm (DASSF) for small target detection in aerial images. Experimental results show that when the DASSF method is applied to YOLOv8, compared to YOLOv8n, the model shows an increase of 9.2% and 2.4% in the mean average precision (mAP)
  arXiv  Detail & Related papers  (2024-06-18T05:26:44Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection [7.262048441360132]
  Few-shot object detection is an emerging topic in the area of few-shot learning and object detection. We propose a training scheme that allows learning inductive biases that can boost few-shot detection. The proposed approach yields interpretable loss functions, as opposed to highly parametric and complex few-shot meta-models.
  arXiv  Detail & Related papers  (2023-04-24T15:14:16Z)
• ARS-DETR: Aspect Ratio-Sensitive Detection Transformer for Aerial Oriented Object Detection [55.291579862817656]
  Existing oriented object detection methods commonly use metric AP$_50$ to measure the performance of the model. We argue that AP$_50$ is inherently unsuitable for oriented object detection due to its large tolerance in angle deviation. We propose an Aspect Ratio Sensitive Oriented Object Detector with Transformer, termed ARS-DETR, which exhibits a competitive performance.
  arXiv  Detail & Related papers  (2023-03-09T02:20:56Z)
• Chosen methods of improving object recognition of small objects with weak recognizable features [0.0]
  Using proper GAN model would enable augmenting low precision data increasing their amount and diversity. In this work the GAN-based method with augmentation is presented to improve small object detection on VOC Pascal dataset.
  arXiv  Detail & Related papers  (2022-08-29T13:39:02Z)
• Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions [65.84090965167535]
  We present Neural Motion Fields, a novel object representation which encodes both object point clouds and the relative task trajectories as an implicit value function parameterized by a neural network. This object-centric representation models a continuous distribution over the SE(3) space and allows us to perform grasping reactively by leveraging sampling-based MPC to optimize this value function.
  arXiv  Detail & Related papers  (2022-06-29T18:47:05Z)
• Focused Adversarial Attacks [1.607104211283248]
  Recent advances in machine learning show that neural models are vulnerable to minimally perturbed inputs, or adversarial examples. We propose to use a very limited subset of a model's learned manifold to compute adversarial examples. Our textitFocused Adversarial Attacks (FA) algorithm identifies a small subset of sensitive regions to perform gradient-based adversarial attacks.
  arXiv  Detail & Related papers  (2022-05-19T15:38:23Z)
• Real-World Semantic Grasping Detection [0.34410212782758054]
  We propose an end-to-end semantic grasping detection model, which can accomplish both semantic recognition and grasping detection. We also design a target feature filtering mechanism, which only maintains the features of a single object according to the semantic information for grasping detection. Experimental results show that the proposed method can achieve 98.38% accuracy in Cornell grasping dataset.
  arXiv  Detail & Related papers  (2021-11-20T05:57:22Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)
• Incremental Object Detection via Meta-Learning [77.55310507917012]
  We propose a meta-learning approach that learns to reshape model gradients, such that information across incremental tasks is optimally shared. In comparison to existing meta-learning methods, our approach is task-agnostic, allows incremental addition of new-classes and scales to high-capacity models for object detection.
  arXiv  Detail & Related papers  (2020-03-17T13:40:00Z)

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.