Obstacle detection in a field environment based on a convolutional neural network security

Li, Tianping; Xu, Wenhao; Wang, Wen; Zhang, Xiaofeng

doi:10.1080/17517575.2020.1797180

Cited by 8 publications

(5 citation statements)

References 28 publications

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“…These are among the major difficulties of obstacle detection in real-world mobile robot navigation that have resulted in many environment-related accidents. Object detection algorithms use the explicit definition of environment variables like obstacle size, shape, depth, and object distance range in the environment [ 55 , 57 ] to predict obstacles, improve obstacle avoidance, and plan a smooth route to the destination. Additionally, Table 3 highlights ROS deep learning solutions that have been developed and can be applicable based on their capabilities.…”

Section: Learning-based Navigation Techniques (Methods)mentioning

confidence: 99%

“…CNN represents a deep learning architecture composed of multiple convolutional layers. This method demonstrates the capacity to automatically discover and extract important elements from images, facilitating object recognition and classification [ 55 ]. Numerous researchers have used this method to develop models for object detection and collision avoidance.…”

Section: Learning-based Navigation Techniques (Methods)mentioning

confidence: 99%

“…The subject of autonomous robot navigation entails mapping, localisation, obstacle detection, avoidance, and achieving an optimal path from a starting point to a predefined target location efficiently [ 55 ]. The nature of obstacles encountered can be static or dynamic, depending on the environment structure.…”

Section: Concept and Backgroundmentioning

confidence: 99%

See 2 more Smart Citations

Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

Adiuku,

Avdelidis,

Tang

et al. 2024

Sensors

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The field of learning-based navigation for mobile robots is experiencing a surge of interest from research and industry sectors. The application of this technology for visual aircraft inspection tasks within a maintenance, repair, and overhaul (MRO) hangar necessitates efficient perception and obstacle avoidance capabilities to ensure a reliable navigation experience. The present reliance on manual labour, static processes, and outdated technologies limits operation efficiency in the inherently dynamic and increasingly complex nature of the real-world hangar environment. The challenging environment limits the practical application of conventional methods and real-time adaptability to changes. In response to these challenges, recent years research efforts have witnessed advancement with machine learning integration aimed at enhancing navigational capability in both static and dynamic scenarios. However, most of these studies have not been specific to the MRO hangar environment, but related challenges have been addressed, and applicable solutions have been developed. This paper provides a comprehensive review of learning-based strategies with an emphasis on advancements in deep learning, object detection, and the integration of multiple approaches to create hybrid systems. The review delineates the application of learning-based methodologies to real-time navigational tasks, encompassing environment perception, obstacle detection, avoidance, and path planning through the use of vision-based sensors. The concluding section addresses the prevailing challenges and prospective development directions in this domain.

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Section: Learning-based Navigation Techniques (Methods)mentioning

confidence: 99%

Section: Learning-based Navigation Techniques (Methods)mentioning

confidence: 99%

See 1 more Smart Citation

Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

Adiuku,

Avdelidis,

Tang

et al. 2024

Sensors

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show abstract

“…In common neural networks, the sum of each node in the first layer is weighted on the common neural network, and the initial value of the last layer can be regarded as a representation or function to learn the neural network from the input data [13]. In practice, a CNN has the ability of representation learning by improving the architecture of the common neural network.…”

Section: Deep Learning Techniquesmentioning

confidence: 99%

Deep Learning Anomaly Detection Based on Hierarchical Status-Connection Features in Networked Control Systems

Zhao¹,

Zeng²,

Chen³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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As networked control systems continue to be widely used in large-scale industrial productions, industrial cyber-attacks have become an inevitable problem that can cause serious damage to critical infrastructures. In practice, industrial intrusion detection has been widely acknowledged to detect abnormal communication behaviors. However, unlike traditional IT systems, networked control systems have their own communication characteristics due to specific industrial communication protocols. Thus, simple cyber-attack modeling is inadequate and impractical for high-efficiency intrusion detection because the characteristics of network control systems are less considered. Based on the status information and transmission connection in industrial communication data payloads, which can properly express the characteristics of industrial control logic, this paper associates industrial communication features with transmission connection payload and status payload. Furthermore, transmission connection features include device address, context, time, and packet length, while status features cover measurement, input, distributed state, control state, and more. After designing a convolutional neural network (CNN) and a long short-term memory network (LSTM) to extract status features and transmission connection features from industrial communication data, this paper proposes a hierarchical deep learning anomaly detection approach, which can integrate the advantages of CNN and LSTM to achieve high-efficiency detection. The experimental results clearly show that the proposed approach, having the advantages of strong detection capability and low false alarm rate, is a superior means of anomaly detection when compared to its peers.

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“…Although these methods show good performance in a specific complex detection environment, they mainly rely on manual or shallow feature extraction, resulting in significant limitations and poor robustness. In recent years, the convolutional neural network(CNN) in the deep learning method [9] has been widely utilized in diverse scenarios such as industrial surveying [10] and object detection [11] because of its superiority, and it has also become the mainstream of crack detection [12](e.g., Qu et al [13], Nguyen et al [14], Han et al [15]). In object detection, although the position of the crack can be located [16][17], its…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network with Equal-Resolution Enhancement and Gradual Attention of Features for Tiny Target Detection

Cheng

Wang²,

Zhou

et al. 2022

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)

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The detection of tiny targets on the surface with high efficiency and accuracy is significant for the current intelligent manufacturing. Visual inspection methods based on deep learning are widely utilized to detect tiny objects. However, the tiny objects appear less distinct, less wide, and less area occupied in the image. At the same time, there is a lot of object-like noise, which further increases the difficulty of detecting tiny objects. In response to the challenges brought by the complexity of the detection environment, this paper proposes a network model that combines the enhancement of pixel-level features at equal resolution and the introduction of full-scale features based on attention. The model utilizes the subtle differences between the tiny target and the background and the semantic information of the tiny target outline to enhance the features of the tiny target while significantly reducing its loss in the equal-resolution feature layer. Additionally, a gradual attention mechanism is proposed to guide the network model to pay attention to tiny objects features on the full-scale feature layer. The performance of this network model is validated on a real dataset. Experiments show that the model exhibits superior performance and outperforms existing resNet50, DenseNet, Racki-Net, and SegDecNet in detecting tiny objects.

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Obstacle detection in a field environment based on a convolutional neural network security

Cited by 8 publications

References 28 publications

Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review

Deep Learning Anomaly Detection Based on Hierarchical Status-Connection Features in Networked Control Systems

A Convolutional Neural Network with Equal-Resolution Enhancement and Gradual Attention of Features for Tiny Target Detection

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