YOLOv5 Drone Detection Using Multimodal Data Registered by the Vicon System

Lindenheim-Locher, Wojciech; Świtoński, Adam; Krzeszowski, Tomasz; Paleta, Grzegorz; Hasiec, Piotr; Josiński, Henryk; Paszkuta, Marcin; Wojciechowski, Konrad; Rosner, Jakub

doi:10.3390/s23146396

Cited by 6 publications

(3 citation statements)

References 30 publications

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“… Park (2023) explored CNNs integrated with multimodal information, extracting features from comments and images to accurately predict customers’ revisiting behavior. Lindenheim-Locher et al (2023) focused on real-time multimodal 3D CNNs, emphasizing their pivotal role in processing higher-resolution images and consequently enhancing 3D image detection capacities. In accordance with prior research, it becomes readily apparent that DL algorithms have yielded remarkable achievements within the realm of IoT image object detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Object detection in optical imaging of the Internet of Things based on deep learning

Chen,

Hei,

Lai

2023

PeerJ Computer Science

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This article endeavors to enhance image recognition technology within the context of the Internet of Things (IoT). A dynamic image target detection training model is established through the convolutional neural network (CNN) algorithm within the framework of deep learning (DL). Three distinct model configurations are proposed: a nine-layer convolution model, a seven-layer convolution model, and a residual module convolution model. Subsequently, the simulation model of CNN image target detection based on optical imaging is constructed, and the simulation experiments are conducted in scenarios of simple and salient environments, complex and salient environments, and intricate micro-environment. By determining the optimal training iterations, comparisons are drawn in terms of precision, accuracy, Intersection Over Union (IoU), and frames per second (FPS) among different model configurations. Finally, an attention mechanism is incorporated within the DL framework, leading to the construction of an attention mechanism CNN target detection model that operates at three difficulty levels: simple, intermediate, and challenging. Through comparative analysis against prevalent target detection algorithms, this article delves into the accuracy and detection efficiency of various models for IoT target detection. Key findings include: (1) The seven-layer CNN model exhibits commendable accuracy and confidence in simple and salient environments, although it encounters certain instances of undetected images, indicating scope for improvement. (2) The residual network model, when employing a loss function comprising both mean square error (MSE) and cross entropy, demonstrates superior performance in complex and salient environments, manifesting high precision, IoU, and accuracy metrics, thereby establishing itself as a robust detection model. (3) Within intricate micro-environments, the residual CNN model, utilizing loss functions of MSE and cross entropy, yields substantial results, with precision, IoU, and FPS values amounting to 0.99, 0.83, and 29.9, respectively. (4) The CNN model enriched with an attention mechanism outperforms other models in IoT target image detection, achieving the highest accuracy rates of 24.86%, 17.8%, and 14.77% in the simple, intermediate, and challenging levels, respectively. Although this model entails slightly longer detection times, its overall detection performance is excellent, augmenting the effectiveness of object detection within IoT. This article strives to enhance image target detection accuracy and speed, bolster the recognition capability of IoT systems, and refine dynamic image target detection within IoT settings. The implications encompass reduced manual recognition costs and the provision of a theoretical foundation for optimizing imaging and image target detection technologies in the IoT context.

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Section: Literature Reviewmentioning

confidence: 99%

Object detection in optical imaging of the Internet of Things based on deep learning

Chen,

Hei,

Lai

2023

PeerJ Computer Science

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“…Different versions of the YOLOv5 model are available, varying in terms of network depth and width, including sizes such as "s," "m," "l," and "x," and also come in versions like 1.0, 5.0, and 6.0. Taking into consideration the relatively small size of the homemade vehicle black smoke dataset used in this study and the real-time detection requirements, the decision was made to choose YOLOv5s version 6.0 as the base network for lightweight improvements [18,19]. The YOLOv5s model is structured in four main parts: the input layer, the backbone network, the neck network, and the output layer.…”

Section: Yolov5 Network Frameworkmentioning

confidence: 99%

A Lightweight Model for Real-Time Detection of Vehicle Black Smoke

Chen,

Wang,

Zhai

2023

Sensors

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This paper discusses the application of deep learning technology in recognizing vehicle black smoke in road traffic monitoring videos. The use of massive surveillance video data imposes higher demands on the real-time performance of vehicle black smoke detection models. The YOLOv5s model, known for its excellent single-stage object detection performance, has a complex network structure. Therefore, this study proposes a lightweight real-time detection model for vehicle black smoke, named MGSNet, based on the YOLOv5s framework. The research involved collecting road traffic monitoring video data and creating a custom dataset for vehicle black smoke detection by applying data augmentation techniques such as changing image brightness and contrast. The experiment explored three different lightweight networks, namely ShuffleNetv2, MobileNetv3 and GhostNetv1, to reconstruct the CSPDarknet53 backbone feature extraction network of YOLOv5s. Comparative experimental results indicate that reconstructing the backbone network with MobileNetv3 achieved a better balance between detection accuracy and speed. The introduction of the squeeze excitation attention mechanism and inverted residual structure from MobileNetv3 effectively reduced the complexity of black smoke feature fusion. Simultaneously, a novel convolution module, GSConv, was introduced to enhance the expression capability of black smoke features in the neck network. The combination of depthwise separable convolution and standard convolution in the module further reduced the model’s parameter count. After the improvement, the parameter count of the model is compressed to 1/6 of the YOLOv5s model. The lightweight vehicle black smoke real-time detection network, MGSNet, achieved a detection speed of 44.6 frames per second on the test set, an increase of 18.9 frames per second compared with the YOLOv5s model. The mAP@0.5 still exceeded 95%, meeting the application requirements for real-time and accurate detection of vehicle black smoke.

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“…These issues underline the need for diverse and comprehensive training datasets to enhance the accuracy and reliability of deep learning-based drone detection systems. Addressing these data collection challenges and managing costs are essential for improving the performance of deep learning models like YOLO in drone detection [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility

Ywet,

Maw,

Nguyen

et al. 2024

Aerospace

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Urban Air Mobility (UAM) emerges as a transformative approach to address urban congestion and pollution, offering efficient and sustainable transportation for people and goods. Central to UAM is the Operational Digital Twin (ODT), which plays a crucial role in real-time management of air traffic, enhancing safety and efficiency. This study introduces a YOLOTransfer-DT framework specifically designed for Artificial Intelligence (AI) training in simulated environments, focusing on its utility for experiential learning in realistic scenarios. The framework’s objective is to augment AI training, particularly in developing an object detection system that employs visual tasks for proactive conflict identification and mission support, leveraging deep and transfer learning techniques. The proposed methodology combines real-time detection, transfer learning, and a novel mix-up process for environmental data extraction, tested rigorously in realistic simulations. Findings validate the use of existing deep learning models for real-time object recognition in similar conditions. This research underscores the value of the ODT framework in bridging the gap between virtual and actual environments, highlighting the safety and cost-effectiveness of virtual testing. This adaptable framework facilitates extensive experimentation and training, demonstrating its potential as a foundation for advanced detection techniques in UAM.

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YOLOv5 Drone Detection Using Multimodal Data Registered by the Vicon System

Cited by 6 publications

References 30 publications

Object detection in optical imaging of the Internet of Things based on deep learning

Object detection in optical imaging of the Internet of Things based on deep learning

A Lightweight Model for Real-Time Detection of Vehicle Black Smoke

YOLOTransfer-DT: An Operational Digital Twin Framework with Deep and Transfer Learning for Collision Detection and Situation Awareness in Urban Aerial Mobility

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