Real-time object detection method based on improved YOLOv4-tiny

The "You only look once v4"(YOLOv4) is one type of object detection methods in deep learning. YOLOv4-tiny is proposed based on YOLOv4 to simple the network structure and reduce parameters, which makes it be suitable for developing on the mobile and embedded devices. To improve the real-time of object detection, a fast object detection method is proposed based on YOLOv4-tiny. It firstly uses two ResBlock-D modules in ResNet-D network instead of two CSPBlock modules in Yolov4-tiny, which reduces the computation complexity. Secondly, it designs an auxiliary residual network block to extract more feature information of object to reduce detection error. In the design of auxiliary network, two consecutive 3x3 convolutions are used to obtain 5x5 receptive fields to extract global features, and channel attention and spatial attention are also used to extract more effective information. In the end, it merges the auxiliary network and backbone network to construct the whole network structure of improved YOLOv4-tiny. Simulation results show that the proposed method has faster object detection than YOLOv4-tiny and YOLOv3-tiny, and almost the same mean value of average precision as the YOLOv4-tiny. It is more suitable for real-time object detection.Comment: 14pages,7figures,2table

Efficient Object Detection and Intelligent Information Display Using YOLOv4-Tiny

This study aims to develop an innovative image recognition and information display approach based on you only look once version 4 (YOLOv4)-tiny framework. The lightweight YOLOv4-tiny model is modified by replacing convolutional modules with Fire modules to further reduce its parameters. Performance reductions are offset by including spatial pyramid pooling, and they also improve the model’s detection ability for objects of various sizes. The pattern analysis, statistical modeling, and computational learning visual object classes (PASCAL VOC) 2012 dataset are used, the proposed modified YOLOv4-tiny architecture achieves a higher mean average precision (mAP) that is 1.59% higher than its unmodified counterpart. This study addresses the need for efficient object detection and recognition on resource-constrained devices by leveraging YOLOv4-tiny, Fire modules, and SPP to achieve accurate image recognition at a low computational cost

Comunicações com câmara para aplicações de platooning

Platooning is a technology that corresponds to all the coordinated movements of a collection of vehicles, or, in the case of mobile robotics, to all the coordinated movements of a collection of mobile robots. It brings several advantages to driving, such as, improved safety, accurate speed control, lower CO2 emission rates, and higher energy efficiency. This dissertation describes the development of a laboratory scale demonstrator of platooning based on optical camera communications, using two generic wheel steered robots. For this purpose, one of the robots is equipped with a Light Emitting Diode (LED) matrix and the other with a camera. The LED matrix acts as an Optical Camera Communication (OCC) transmitter, providing status information of the robot attitude. The camera acts as both image acquisition and as an OCC receiver. The gathered information is processed using the algorithm You Only Look Once (YOLO) to infer the robot motion. The YOLO object detector continuously checks the movement of the robot in front. Performance evaluation of 5 different YOLO models (YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, YOLOv4-tiny-3l) was conducted to assess which model works best for this project. The outcomes demonstrate that YOLOv4-tiny surpasses the other models in terms of timing, making it the ideal choice for real-time performance. Object detection using YOLOv4-tiny was performed on the computer. This was chosen since it has a processing speed of 3.09 fps as opposed to the Raspberry Pi’s 0.2 fps.O platooning é uma tecnologia que corresponde a todas as movimentações coordenadas de um conjunto de veículos, ou, no caso da robótica movel, a todas as movimentações coordenadas de um conjunto de robots móveis. Traz várias vantagens para a condução, tais como, maior segurança, um controlo preciso da velocidade, menores taxas de emissão de CO2 e maior eficiência energética. Esta dissertação descreve o desenvolvimento de um demonstrador de platooning em escala laboratorial baseado em comunicações com câmera, usando dois robôs móveis genéricos. Para este propósito, um dos robôs é equipado com uma matriz de Light Emitting Diodes (LEDs) e o outro é equipado com uma câmera. A matriz de LEDs funciona como transmissor, fornecendo informações de estado do robô. A câmera funciona como recetor, realizando a aquisição de imagens. As informações recolhidas são processadas usando o algoritmo You Only Look Once (YOLO) de forma a prever o movimento do robô. O YOLO verifica continuamente o movimento do robô da frente. A avaliação de desempenho de 5 modelos de YOLO diferentes (YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, YOLOv4-tiny-3l) foi realizada para identificar qual o modelo que funciona melhor no contexto deste projeto. Os resultados demonstram que o YOLOv4-tiny supera os outros modelos em termos de tempo, tornando-o a escolha ideal para desempenho em tempo real. A deteção de objetos usando YOLOv4-tiny foi realizada no computador. Esta escolhe deveuse ao facto de o computador ter uma velocidade de processamento de 3,09 fps em oposição aos 0,2 fps da Raspberry Pi.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Computational classification of animals for a highway detection system

As colisões entre veículos e animais representam um sério problema na infraestrutura rodoviária. Para evitar tais acidentes, medidas mitigatórias têm sido aplicadas em diferentes regiões do mundo. Neste projeto é apresentado um sistema de detecção de animais em rodovias utilizando visão computacional e algoritmo de aprendizado de máquina. Os modelos foram treinados para classificar dois grupos de animais: capivaras e equídeos. Foram utilizadas duas variantes da rede neural convolucional chamada Yolo (você só vê uma vez) — Yolov4 e Yolov4-tiny (versão mais leve da rede) — e o treinamento foi realizado a partir de modelos pré-treinados. Testes de detecção foram realizados em 147 imagens e os resultados de precisão obtidos foram de 84,87% e 79,87% para Yolov4 e Yolov4-tiny, respectivamente. O sistema proposto tem o potencial de melhorar a segurança rodoviária reduzindo ou prevenindo acidentes com animais.Vehicle-animal collisions represent a serious problem in roadway infrastructure. To avoid these roadway collisions, different mitigation systems have been applied in various regions of the world. In this article, a system for detecting animals on highways is presented using computer vision and machine learning algorithms. The models were trained to classify two groups of animals: capybaras and donkeys. Two variants of the convolutional neural network called Yolo (You only look once) were used, Yolov4 and Yolov4-tiny (a lighter version of the network). The training was carried out using pre-trained models. Detection tests were performed on 147 images. The accuracy results obtained were 84.87% and 79.87% for Yolov4 and Yolov4-tiny, respectively. The proposed system has the potential to improve road safety by reducing or preventing accidents with animals

Pendeteksian Senjata Api pada Manusia dalam Situasi Real-Time Menggunakan Model YOLOv4-Tiny

This research aims to develop a real-time human firearm detection system using the YOLOv4-tiny method. The system is implemented and tested on public security CCTV cameras to enhance responses to potential security threats. The research results indicate that the developed detection system achieves an accuracy level of approximately 95%. Real-time testing successfully detects various types of firearms, including rifles, shotguns, and handguns. This success demonstrates the potential of YOLOv4-tiny as an effective solution for improving public safety with fast and accurate firearm detection. The research makes a significant contribution to security technology development, offering an efficient means to prevent violent incidents and protect communities effectively

Design Human Object Detection Yolov4-Tiny Algorithm on ARM Cortex-A72 and A53

Currently, many object detection systems still use devices with large sizes, such as using PCs, as supporting devices, for object detection. This makes these devices challenging to use as a security system in public facilities based on human object detection. In contrast, many Mini PCs currently use ARM processors with high specifications. In this research, to detect human objects will use the Mini PC Nanopi M4V2 device that has a speed in processing with the support of CPU Dual-Core Cortex-A72 (up to 2.0 GHz) + Cortex A53 (Up to 2.0 GHz) and 4 Gb DDR4 Ram. In addition, for the human object detection system, the author uses the You Only Look Once (YOLO) method with the YoloV4-Tiny type, With these specifications and methods, the detection rate and FPS score are seen which are the feasibility values for use in detecting human objects. The simulation for human object recognition was carried out using recorded video, simulation obtained a detection rate of 0,9845 or 98% with FPS score of 3.81-5.55.  These results are the best when compared with the YOLOV4 and YOLOV5 models. With these results, it can be applied in various human detection applications and of course robustness testing is needed

A mobile application for detecting and monitoring the development stages of wild flowers and plants

Wild flowers and plants appear spontaneously. They form the ecological basis on which life depends. They play a fundamental role in the regeneration of natural life and the balance of ecological systems. However, this irreplaceable natural heritage is at risk of being lost due to human activity and climate change. The work presented in this paper contributes to the conservation effort. It is based on a previous study by the same authors, which identified computer vision as a suitable technological platform for detecting and monitoring the development stages of wild flowers and plants. It describes the process of developing a mobile application that uses YOLOv4 and YOLOv4-tiny convolutional neural networks to detect the stages of development of wild flowers and plants. This application could be used by visitors in a nature park to provide information and raise awareness about the wild flowers and plants they find along the roads and trails.J.M.L.P.C. and V.N.G.J.S. acknowledge that this work is funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/50008/2020. P.D.G. thanks the support provided by the Center for Mechanical and Aerospace Science and Technologies (C-MAST) under project UIDB/00151/2020. This is within the activities of project Montanha Viva – An intelligent prediction system for decision support in sustainability, project PD21-00009, promoted by PROMOVE program funded by Fundação La Caixa and supported by Fundação para a Ciência e a Tecnologia and BPI.info:eu-repo/semantics/publishedVersio

A Deep Learning-Based Hybrid Approach to Detect Fastener Defects in Real-Time

A fastener is an important component used to fix the rail in railways. Defects in this component cause the rail and ballast to remain unstable. If the defective fasteners are not replaced in time, it is inevitable that the train will derail, and serious accidents will occur. Therefore, this component should be inspected periodically. Conventional image processing-based control systems are affected by noise and different lighting conditions in the real environment. In this study, it is aimed to determine the defects of fasteners with a deep learning-based hybrid approach. The YOLOv4-Tiny method is used for fastener detection and localization. This method gives accurate results, especially for the detection of small objects. After the fastener position is determined, a new lightweight convolutional neural network model is used for defect classification. The proposed convolutional neural network for classification has a small network structure because it uses depth-wise and pointwise convolution layers. When the experimental results are compared with other known transfer learning methods, better results were obtained in terms of training/test time and accuracy

A deep neural network approach

Unmanned Aerial Vehicle (UAV) images can be an important resource when performing Search And Rescue (SAR) operations at sea. With the improving technology UAVs are becoming an accessible and fairly inexpensive resource for many applications such as SAR. In order to maximize the usefulness of these UAV, we propose a method which utilizes a state-of-the-art Object Detection network to perform real-time detection on-board the UAV. In this thesis we have selected the YOLOv4-tiny Object Detection network and trained it to detect castaways at sea. The main goal is to obtained fully trained weights that can be utilised with the YOLOv4-tiny and applied to use in SAR with several UAVs working in parallel that report back to a human operator upon detection of a possible castaway. The proposed approach has been validated on a test dataset obtained for the purpose of this thesis and the final result shows that it has good capabilities that can be further developedImagens de veículos aéreos não tripulados (UAV) podem ser um recurso importante para a realização de operações de busca e salvamento (SAR) no mar. Com os avanços da tecnologia, os UAVs têm-se tornado um recurso acessível e razoavelmente barato para muitas aplicações como SAR. A fim de maximizar a utilidade destes UAV, propôs-se um método que utiliza uma rede de detecção de objetos de última geração para realizar a detecção em tempo real a bordo do UAV. Nesta tese, selecionou-se a rede de detecção de objetos YOLOv4-tiny e efetuou-se o seu treino para detectar náufragos no mar. O principal objetivo é obter pesos totalmente treinados que possam ser utilizados com o YOLOv4-tiny e aplicados para uso em SAR com vários UAVs a operar em paralelo que reportam a um operador humano aquando a detecção de um possível náufrago. A abordagem proposta foi validada com um conjunto de dados de teste obtido para o propósito desta tese, e o resultado final mostrou boas capacidades que podem ser ainda mais desenvolvidas