Randomize to Generalize: Domain Randomization for Runway FOD Detection
- URL: http://arxiv.org/abs/2309.13264v1
- Date: Sat, 23 Sep 2023 05:02:31 GMT
- Title: Randomize to Generalize: Domain Randomization for Runway FOD Detection
- Authors: Javaria Farooq, Nayyer Aafaq, M Khizer Ali Khan, Ammar Saleem, M
Ibraheem Siddiqui
- Abstract summary: Tiny Object Detection is challenging due to small size, low resolution, occlusion, background clutter, lighting conditions and small object-to-image ratio.
We propose a novel two-stage methodology Synthetic Image Augmentation (SRIA) to enhance generalization capabilities of models encountering 2D datasets.
We report that detection accuracy improved from an initial 41% to 92% for OOD test set.
- Score: 1.4249472316161877
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Tiny Object Detection is challenging due to small size, low resolution,
occlusion, background clutter, lighting conditions and small object-to-image
ratio. Further, object detection methodologies often make underlying assumption
that both training and testing data remain congruent. However, this presumption
often leads to decline in performance when model is applied to
out-of-domain(unseen) data. Techniques like synthetic image generation are
employed to improve model performance by leveraging variations in input data.
Such an approach typically presumes access to 3D-rendered datasets. In
contrast, we propose a novel two-stage methodology Synthetic Randomized Image
Augmentation (SRIA), carefully devised to enhance generalization capabilities
of models encountering 2D datasets, particularly with lower resolution which is
more practical in real-world scenarios. The first stage employs a weakly
supervised technique to generate pixel-level segmentation masks. Subsequently,
the second stage generates a batch-wise synthesis of artificial images,
carefully designed with an array of diverse augmentations. The efficacy of
proposed technique is illustrated on challenging foreign object debris (FOD)
detection. We compare our results with several SOTA models including CenterNet,
SSD, YOLOv3, YOLOv4, YOLOv5, and Outer Vit on a publicly available FOD-A
dataset. We also construct an out-of-distribution test set encompassing 800
annotated images featuring a corpus of ten common categories. Notably, by
harnessing merely 1.81% of objects from source training data and amalgamating
with 29 runway background images, we generate 2227 synthetic images. Subsequent
model retraining via transfer learning, utilizing enriched dataset generated by
domain randomization, demonstrates significant improvement in detection
accuracy. We report that detection accuracy improved from an initial 41% to 92%
for OOD test set.
Related papers
- Time Step Generating: A Universal Synthesized Deepfake Image Detector [0.4488895231267077]
We propose a universal synthetic image detector Time Step Generating (TSG)
TSG does not rely on pre-trained models' reconstructing ability, specific datasets, or sampling algorithms.
We test the proposed TSG on the large-scale GenImage benchmark and it achieves significant improvements in both accuracy and generalizability.
arXiv Detail & Related papers (2024-11-17T09:39:50Z) - Semi-Truths: A Large-Scale Dataset of AI-Augmented Images for Evaluating Robustness of AI-Generated Image detectors [62.63467652611788]
We introduce SEMI-TRUTHS, featuring 27,600 real images, 223,400 masks, and 1,472,700 AI-augmented images.
Each augmented image is accompanied by metadata for standardized and targeted evaluation of detector robustness.
Our findings suggest that state-of-the-art detectors exhibit varying sensitivities to the types and degrees of perturbations, data distributions, and augmentation methods used.
arXiv Detail & Related papers (2024-11-12T01:17:27Z) - DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
Current perceptive models heavily depend on resource-intensive datasets.
We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability.
Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
arXiv Detail & Related papers (2024-03-20T04:58:03Z) - SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised
Learning for Robust Infrared Small Target Detection [53.19618419772467]
Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds.
With the development of Transformer, the scale of SIRST models is constantly increasing.
With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed.
arXiv Detail & Related papers (2024-03-08T16:14:54Z) - Real-Time Object Detection in Occluded Environment with Background
Cluttering Effects Using Deep Learning [0.8192907805418583]
We concentrate on deep learning models for real-time detection of cars and tanks in an occluded environment with a cluttered background.
The developed method makes the custom dataset and employs a preprocessing technique to clean the noisy dataset.
The accuracy and frame per second of the SSD-Mobilenet v2 model are higher than YOLO V3 and YOLO V4.
arXiv Detail & Related papers (2024-01-02T01:30:03Z) - 3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features [70.50665869806188]
3DiffTection is a state-of-the-art method for 3D object detection from single images.
We fine-tune a diffusion model to perform novel view synthesis conditioned on a single image.
We further train the model on target data with detection supervision.
arXiv Detail & Related papers (2023-11-07T23:46:41Z) - Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors [14.835051543002164]
We train a Diffusion Model to bridge the Sim2Real gap in the field of vision-based tactile sensors for classifying object surfaces.
We employ a simulator to generate images by uniformly sampling the surface of objects from the YCB Model Set.
These simulated images are then translated into the real domain using the Diffusion Model and automatically labeled to train a classifier.
arXiv Detail & Related papers (2023-11-02T16:37:27Z) - Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations.
In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
arXiv Detail & Related papers (2023-08-31T05:05:53Z) - Explore the Power of Synthetic Data on Few-shot Object Detection [27.26215175101865]
Few-shot object detection (FSOD) aims to expand an object detector for novel categories given only a few instances for training.
Recent text-to-image generation models have shown promising results in generating high-quality images.
This work extensively studies how synthetic images generated from state-of-the-art text-to-image generators benefit FSOD tasks.
arXiv Detail & Related papers (2023-03-23T12:34:52Z) - Lafite2: Few-shot Text-to-Image Generation [132.14211027057766]
We propose a novel method for pre-training text-to-image generation model on image-only datasets.
It considers a retrieval-then-optimization procedure to synthesize pseudo text features.
It can be beneficial to a wide range of settings, including the few-shot, semi-supervised and fully-supervised learning.
arXiv Detail & Related papers (2022-10-25T16:22:23Z) - Road Segmentation for Remote Sensing Images using Adversarial Spatial
Pyramid Networks [28.32775611169636]
We introduce a new model to apply structured domain adaption for synthetic image generation and road segmentation.
A novel scale-wise architecture is introduced to learn from the multi-level feature maps and improve the semantics of the features.
Our model achieves state-of-the-art 78.86 IOU on the Massachusetts dataset with 14.89M parameters and 86.78B FLOPs, with 4x fewer FLOPs but higher accuracy (+3.47% IOU)
arXiv Detail & Related papers (2020-08-10T11:00:19Z)
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.