Small Object Detection using Deep Learning
- URL: http://arxiv.org/abs/2201.03243v1
- Date: Mon, 10 Jan 2022 09:58:25 GMT
- Title: Small Object Detection using Deep Learning
- Authors: Aleena Ajaz, Ayesha Salar, Tauseef Jamal, Asif Ullah Khan
- Abstract summary: The proposed system consists of a custom deep learning model Tiny YOLOv3, one of the flavors of very fast object detection model You Look Only Once (YOLO) is built and used for detection.
The proposed architecture has shown significantly better performance as compared to the previous YOLO version.
- Score: 0.28675177318965034
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Now a days, UAVs such as drones are greatly used for various purposes like
that of capturing and target detection from ariel imagery etc. Easy access of
these small ariel vehicles to public can cause serious security threats. For
instance, critical places may be monitored by spies blended in public using
drones. Study in hand proposes an improved and efficient Deep Learning based
autonomous system which can detect and track very small drones with great
precision. The proposed system consists of a custom deep learning model Tiny
YOLOv3, one of the flavors of very fast object detection model You Look Only
Once (YOLO) is built and used for detection. The object detection algorithm
will efficiently the detect the drones. The proposed architecture has shown
significantly better performance as compared to the previous YOLO version. The
improvement is observed in the terms of resource usage and time complexity. The
performance is measured using the metrics of recall and precision that are 93%
and 91% respectively.
Related papers
- SOAR: Self-supervision Optimized UAV Action Recognition with Efficient Object-Aware Pretraining [65.9024395309316]
We introduce a novel Self-supervised pretraining algorithm for aerial footage captured by Unmanned Aerial Vehicles (UAVs)
We incorporate human object knowledge throughout the pretraining process to enhance UAV video pretraining efficiency and downstream action recognition performance.
arXiv Detail & Related papers (2024-09-26T21:15:22Z) - DroBoost: An Intelligent Score and Model Boosting Method for Drone Detection [1.2564343689544843]
Drone detection is a challenging object detection task where visibility conditions and quality of the images may be unfavorable.
Our work improves on the previous approach by combining several improvements.
The proposed technique won 1st Place in the Drone vs. Bird Challenge.
arXiv Detail & Related papers (2024-06-30T20:49:56Z) - YOLO-FEDER FusionNet: A Novel Deep Learning Architecture for Drone Detection [4.281091463408282]
We introduce a novel deep learning architecture called YOLO-FEDER FusionNet.
Unlike conventional approaches, YOLO-FEDER FusionNet combines generic object detection methods with the specialized strength of camouflage object detection techniques to enhance drone detection capabilities.
arXiv Detail & Related papers (2024-06-17T15:25:31Z) - 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) - Reward Finetuning for Faster and More Accurate Unsupervised Object
Discovery [64.41455104593304]
Reinforcement Learning from Human Feedback (RLHF) can improve machine learning models and align them with human preferences.
We propose to adapt similar RL-based methods to unsupervised object discovery.
We demonstrate that our approach is not only more accurate, but also orders of magnitudes faster to train.
arXiv Detail & Related papers (2023-10-29T17:03:12Z) - 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) - Lightweight Multi-Drone Detection and 3D-Localization via YOLO [1.284647943889634]
We present and evaluate a method to perform real-time multiple drone detection and three-dimensional localization.
We use state-of-the-art tiny-YOLOv4 object detection algorithm and stereo triangulation.
Our computer vision approach eliminates the need for computationally expensive stereo matching algorithms.
arXiv Detail & Related papers (2022-02-18T09:41:23Z) - YOLO-Z: Improving small object detection in YOLOv5 for autonomous
vehicles [5.765622319599904]
This study explores how the popular YOLOv5 object detector can be modified to improve its performance in detecting smaller objects.
We propose a series of models at different scales, which we name YOLO-Z', and which display an improvement of up to 6.9% in mAP when detecting smaller objects at 50% IOU.
Our objective is to inform future research on the potential of adjusting a popular detector such as YOLOv5 to address specific tasks.
arXiv Detail & Related papers (2021-12-22T11:03:43Z) - Track Boosting and Synthetic Data Aided Drone Detection [0.0]
Our method approaches the drone detection problem by fine-tuning a YOLOv5 model with real and synthetically generated data.
Our results indicate that augmenting the real data with an optimal subset of synthetic data can increase the performance.
arXiv Detail & Related papers (2021-11-24T10:16:27Z) - A dataset for multi-sensor drone detection [67.75999072448555]
The use of small and remotely controlled unmanned aerial vehicles (UAVs) has increased in recent years.
Most studies on drone detection fail to specify the type of acquisition device, the drone type, the detection range, or the dataset.
We contribute with an annotated multi-sensor database for drone detection that includes infrared and visible videos and audio files.
arXiv Detail & Related papers (2021-11-02T20:52:03Z) - Dogfight: Detecting Drones from Drones Videos [58.158988162743825]
This paper attempts to address the problem of drones detection from other flying drones variations.
The erratic movement of the source and target drones, small size, arbitrary shape, large intensity, and occlusion make this problem quite challenging.
To handle this, instead of using region-proposal based methods, we propose to use a two-stage segmentation-based approach.
arXiv Detail & Related papers (2021-03-31T17:43:31Z)
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.