CNN-based Human Detection for UAVs in Search and Rescue

• URL: http://arxiv.org/abs/2110.01930v1
• Date: Tue, 5 Oct 2021 10:43:10 GMT
• Title: CNN-based Human Detection for UAVs in Search and Rescue
• Authors: Nikite Mesvan
• Abstract summary: This paper proposes an approach for the first task of searching and detecting victims using a type of convolutional neural network technique. The model used in the research is a pre-trained model and is applied to test on a Raspberry Pi model B, which is attached on a Quadcopter. Experimental results proved that the Quadcopter is able to stably flight and the SSD model works well on the Raspberry Pi model B with a processing speed of 3 fps.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The use of Unmanned Aerial Vehicles (UAVs) as a substitute for ordinary vehicles in applications of search and rescue is being studied all over the world due to its flexible mobility and less obstruction, including two main tasks: search and rescue. This paper proposes an approach for the first task of searching and detecting victims using a type of convolutional neural network technique, the Single Shot Detector (SSD) model, with the Quadcopter hardware platform, a type of UAVs. The model used in the research is a pre-trained model and is applied to test on a Raspberry Pi model B, which is attached on a Quadcopter, while a single camera is equipped at the bottom of the Quadcopter to look from above for search and detection. The Quadcopter in this research is a DIY hardware model that uses accelerometer and gyroscope sensors and ultrasonic sensor as the essential components for balancing control, however, these sensors are susceptible to noise caused by the driving forces on the model, such as the vibration of the motors, therefore, the issues about the PID controller, noise processing for the sensors are also mentioned in the paper. Experimental results proved that the Quadcopter is able to stably flight and the SSD model works well on the Raspberry Pi model B with a processing speed of 3 fps and produces the best detection results at the distance of 1 to 20 meters to objects.

Related papers

• RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection [33.07575082922186]
  Three-dimensional object detection is one of the key tasks in autonomous driving. relying solely on cameras is difficult to achieve highly accurate and robust 3D object detection. radar-camera fusion 3D object detection method in the bird's eye view (BEV) RadarBEVNet consists of a dual-stream radar backbone and a Radar Cross-Section (RC) aware BEV encoder.
  arXiv  Detail & Related papers  (2024-03-25T06:02:05Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• 3D Reconstruction of Multiple Objects by mmWave Radar on UAV [15.47494720280318]
  We explore the feasibility of utilizing a mmWave radar sensor installed on a UAV to reconstruct the 3D shapes of multiple objects in a space. The UAV hovers at various locations in the space, and its onboard radar senor collects raw radar data via scanning the space with Synthetic Aperture Radar (SAR) operation. The radar data is sent to a deep neural network model, which outputs the point cloud reconstruction of the multiple objects in the space.
  arXiv  Detail & Related papers  (2022-11-03T21:23:36Z)
• Learning a Single Near-hover Position Controller for Vastly Different Quadcopters [56.37274861303324]
  This paper proposes an adaptive near-hover position controller for quadcopters. It can be deployed to quadcopters of very different mass, size and motor constants. It also shows rapid adaptation to unknown disturbances during runtime.
  arXiv  Detail & Related papers  (2022-09-19T17:55:05Z)
• Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images [96.66271207089096]
  FCOS-LiDAR is a fully convolutional one-stage 3D object detector for LiDAR point clouds of autonomous driving scenes. We show that an RV-based 3D detector with standard 2D convolutions alone can achieve comparable performance to state-of-the-art BEV-based detectors.
  arXiv  Detail & Related papers  (2022-05-27T05:42:16Z)
• Extended Abstract Version: CNN-based Human Detection System for UAVs in Search and Rescue [0.0]
  This paper proposes an approach for the task of searching and detecting human using a convolutional neural network and a Quadcopter hardware platform. A pre-trained CNN model is applied to a Raspberry Pi B and a single camera is equipped at the bottom of the Quadcopter. Experiments proved that the system works well on the Raspberry Pi B with a processing speed of 3 fps.
  arXiv  Detail & Related papers  (2021-11-04T13:57:20Z)
• CNN-based Omnidirectional Object Detection for HermesBot Autonomous Delivery Robot with Preliminary Frame Classification [53.56290185900837]
  We propose an algorithm for optimizing a neural network for object detection using preliminary binary frame classification. An autonomous mobile robot with 6 rolling-shutter cameras on the perimeter providing a 360-degree field of view was used as the experimental setup.
  arXiv  Detail & Related papers  (2021-10-22T15:05:37Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors [66.4525391417921]
  A thermal infrared camera is shown to be a feasible solution to the drone detection task. The detector performance as a function of the sensor-to-target distance is also investigated. A novel video dataset containing 650 annotated infrared and visible videos of drones, birds, airplanes and helicopters is also presented.
  arXiv  Detail & Related papers  (2020-07-14T23:06:42Z)
• Built Infrastructure Monitoring and Inspection Using UAVs and Vision-based Algorithms [2.0305676256390934]
  This study presents an inspecting system using real-time control unmanned aerial vehicles (UAVs) to investigate structural surfaces. The system operates under favourable weather conditions to inspect a target structure, which is the Wentworth light rail base structure in this study.
  arXiv  Detail & Related papers  (2020-05-19T14:37:48Z)
• Mid-flight Propeller Failure Detection and Control of Propeller-deficient Quadcopter using Reinforcement Learning [0.2578242050187029]
  This paper presents reinforcement learning based controllers for quadcopters with 4, 3, and 2 propellers. The paper also proposes a neural network based propeller fault detection system to detect propeller loss and switch to the appropriate controller.
  arXiv  Detail & Related papers  (2020-02-26T15:31:22Z)

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.