Sound-based drone fault classification using multitask learning

• URL: http://arxiv.org/abs/2304.11708v1
• Date: Sun, 23 Apr 2023 17:55:40 GMT
• Title: Sound-based drone fault classification using multitask learning
• Authors: Wonjun Yi, Jung-Woo Choi and Jae-Woo Lee
• Abstract summary: This paper proposes a sound-based deep neural network (DNN) fault classifier and drone sound dataset. The dataset was constructed by collecting the operating sounds of drones from microphones mounted on three different drones in an anechoic chamber. Using the acquired dataset, we train a classifier, 1DCNN-ResNet, that classifies the types of mechanical faults and their locations from short-time input waveforms.
• Score: 7.726132010393797
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The drone has been used for various purposes, including military applications, aerial photography, and pesticide spraying. However, the drone is vulnerable to external disturbances, and malfunction in propellers and motors can easily occur. To improve the safety of drone operations, one should detect the mechanical faults of drones in real-time. This paper proposes a sound-based deep neural network (DNN) fault classifier and drone sound dataset. The dataset was constructed by collecting the operating sounds of drones from microphones mounted on three different drones in an anechoic chamber. The dataset includes various operating conditions of drones, such as flight directions (front, back, right, left, clockwise, counterclockwise) and faults on propellers and motors. The drone sounds were then mixed with noises recorded in five different spots on the university campus, with a signal-to-noise ratio (SNR) varying from 10 dB to 15 dB. Using the acquired dataset, we train a DNN classifier, 1DCNN-ResNet, that classifies the types of mechanical faults and their locations from short-time input waveforms. We employ multitask learning (MTL) and incorporate the direction classification task as an auxiliary task to make the classifier learn more general audio features. The test over unseen data reveals that the proposed multitask model can successfully classify faults in drones and outperforms single-task models even with less training data.

Related papers

• Obfuscated Location Disclosure for Remote ID Enabled Drones [57.66235862432006]
  We propose Obfuscated Location disclOsure for RID-enabled drones (OLO-RID) Instead of disclosing the actual drone's location, drones equipped with OLO-RID disclose a differentially private obfuscated location in a mobile scenario. OLO-RID also extends RID messages with encrypted location information, accessible only by authorized entities.
  arXiv  Detail & Related papers  (2024-07-19T12:35:49Z)
• Drone-type-Set: Drone types detection benchmark for drone detection and tracking [0.6294091730968154]
  In this paper, we provide a dataset of various drones as well as a comparison of recognized object detection models. The experimental results of different models are provided along with a description of each method.
  arXiv  Detail & Related papers  (2024-05-16T18:56:46Z)
• Chasing the Intruder: A Reinforcement Learning Approach for Tracking Intruder Drones [0.08192907805418582]
  We propose a reinforcement learning based approach for identifying and tracking any intruder drone using a chaser drone. Our proposed solution uses computer vision techniques interleaved with the policy learning framework of reinforcement learning. The results show that the reinforcement learning based policy converges to identify and track the intruder drone.
  arXiv  Detail & Related papers  (2023-09-10T16:31:40Z)
• Multi-model fusion for Aerial Vision and Dialog Navigation based on human attention aids [69.98258892165767]
  We present an aerial navigation task for the 2023 ICCV Conversation History. We propose an effective method of fusion training of Human Attention Aided Transformer model (HAA-Transformer) and Human Attention Aided LSTM (HAA-LSTM) models.
  arXiv  Detail & Related papers  (2023-08-27T10:32:52Z)
• Collaborative Learning with a Drone Orchestrator [79.75113006257872]
  A swarm of intelligent wireless devices train a shared neural network model with the help of a drone. The proposed framework achieves a significant speedup in training, leading to an average 24% and 87% saving in the drone hovering time.
  arXiv  Detail & Related papers  (2023-03-03T23:46:25Z)
• Unauthorized Drone Detection: Experiments and Prototypes [0.8294692832460543]
  We present a novel encryption-based drone detection scheme that uses a two-stage verification of the drone's received signal strength indicator ( RSSI) and the encryption key generated from the drone's position coordinates.
  arXiv  Detail & Related papers  (2022-12-02T20:43:29Z)
• TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos [57.92385818430939]
  Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones. Existing methods are computationally costly, follow non-end-to-end optimization, and have complex multi-stage pipelines, making them less suitable for real-time deployment on edge devices. We propose a simple yet effective framework, itTransVisDrone, that provides an end-to-end solution with higher computational efficiency.
  arXiv  Detail & Related papers  (2022-10-16T03:05:13Z)
• Sequence Models for Drone vs Bird Classification [2.294014185517203]
  Drone detection has become an essential task in object detection as drone costs have decreased and drone technology has improved. It is difficult to detect distant drones when there is weak contrast, long range, and low visibility. We propose several sequence classification architectures to reduce the detected false-positive ratio of drone tracks.
  arXiv  Detail & Related papers  (2022-07-21T11:00:44Z)
• 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)
• Scarce Data Driven Deep Learning of Drones via Generalized Data Distribution Space [12.377024173799631]
  We show how understanding the general distribution of the drone data via a Generative Adversarial Network (GAN) can allow us to acquire missing data to achieve rapid and more accurate learning. We demonstrate our results on a drone image dataset, which contains both real drone images as well as simulated images from computer-aided design.
  arXiv  Detail & Related papers  (2021-08-18T17:07:32Z)
• 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.