Atrous spatial pyramid convolution for object detection with encoder-decoder

Jie, Feiran; Nie, Qingfeng; Li, Mingsuo; Yin, Ming; Jin, Taisong

doi:10.1016/j.neucom.2021.07.064

Cited by 20 publications

(4 citation statements)

References 16 publications

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“…The fourth step is nonlinear transformation, which compresses the vector by the Squash function to guarantee that the output's length is between 0 and 1. The dynamic routing algorithm serves as an iterative mechanism facilitating information flow between the primary capsule layer and the digital capsule layer, thereby enhancing the acquisition of useful information [36,37].…”

Section: Capsnetmentioning

confidence: 99%

A multi-scale spatial–temporal capsule network based on sequence encoding for bearing fault diagnosis

Wang,

Chen

2024

Complex Intell. Syst.

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The Capsule Network (CapsNet) has been shown to have significant advantages in improving the accuracy of bearing fault identification. Nevertheless, the CapsNet faces challenges in identifying the type of bearing fault under nonstationary and noisy conditions. These challenges arise from the distinctive nature of its dynamic routing algorithm and the use of fixed single-scale kernels. To address these challenges, a multi-scale spatial–temporal capsule network (MSCN) based on sequence encoding is proposed for bearing fault identification under nonstationary and noisy environments. A spatial–temporal sequence encoding module focuses on feature correlations at various times and positions. Dilated convolution-based multiscale capsule layer (MCaps) is designed to capture spatial–temporal features at different scales. MCaps establishes connections between various layers, enhancing the comprehension and interpretation of spatial–temporal features. Furthermore, the Bhattacharyya coefficient is introduced into the dynamic routing to compare the similarity between capsules. The validity of the model is verified through comparative experiments, and the results show that MSCN has significant advantages over traditional methods.

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Section: Capsnetmentioning

confidence: 99%

A multi-scale spatial–temporal capsule network based on sequence encoding for bearing fault diagnosis

Wang,

Chen

2024

Complex Intell. Syst.

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“…Wang et al [14] proposed a high-precision unmanned aerial vehicle (UAV) localization algorithm based on an encoder-decoder model, utilizing point cloud super-resolution and image semantic segmentation as auxiliary techniques. Jie et al [15] presented a novel Convolutional Neural Network (CNN) model for object detection, which combines Atrous Spatial Pyramid Pooling (ASPP) with an encoder-decoder structure.…”

Section: Encoder-decoder Based Workmentioning

confidence: 99%

AENet: attention enhancement network for industrial defect detection in complex and sensitive scenarios

Wan,

Yi,

Jiang

et al. 2024

J Supercomput

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Conventional image processing and machine learning based on handcrafted features struggle to meet the real-time and high-accuracy requirements for industrial defect detection in complex, sensitive, and dynamic environments. To address this issue, this paper proposes AENet, a novel real-time defect detection network based on an encoder-decoder model, which achieves high detection accuracy and efficiency while demonstrating good convergence and generalization. Firstly, A spatial channel attention (SCA) module in the encoding network is designed to integrate spatial attention and channel attention using a multi-head 3D self-attention mechanism. This improves parallelism and detection efficiency. Secondly, the decoding network of AENet incorporates the Cross-Level Attention Fusion (CLAF) module, which fuses input features from different layers. Combined with multi-level upsampling, this enhances the representation of defect features. Furthermore, we insert a simplified aggregator into the encoder-decoder network of AENet to extract feature information at different scales with low computational cost. This aggregation process aids in training and inference on industrial defect datasets by incorporating contextual information. Extensive experimental results demonstrate that AENet outperforms other segmentation models in accomplishing defect recognition and segmentation in challenging optical environments. It exhibits a faster convergence than other networks and a balance between accuracy and speed. It achieves a recognition precision of over 96% for almost all types of defects in the actual industrial environment on the NVIDIA Tesla V100 GPU.

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“…Most often realized by atrous spatial pyramid pooling Wang et al (2018), spatial pyramid pooling or spatial pyramidbased horizontal feature fusion Jie et al (2021) is also a recommended technique to improve the representation ability of the model. However, the aforementioned spatial pyramids are not optimal for ore sorting networks (lightweight) due to the difficulties of enormous parameters and feature redundancies.…”

Section: Multi-scaled Feature Fusionmentioning

confidence: 99%

Deep learning and IoT enabled digital twin framework for monitoring open-pit coal mines

Yu,

Yang,

Cheng

2023

Front. Energy Res.

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Early detection of cracks enables timely mitigation and maintenance actions, ensuring the safety of personnel and equipment within the open-pit coal mine. Monitoring open-pit coal mines and cracks is essential for the safety of workers and for saving national assets. Digital twins (DTs) can be crucial in open-pit coal mine crack detection. DTs enable continuous real-time monitoring of the open-pit mine, including its structures and surrounding environment. Various sensors and internet-of-things devices can be deployed to collect data on factors such as ground movement and strain. Integrating this data into the DT makes it possible to identify and analyze anomalous behavior or changes that may indicate crack formation or propagation. Deep learning-based networks are a crucial factor in detecting open-pit coal mine cracks. In this work, we propose a deep learning-based densely connected lightweight network incorporated into the DT-based framework for detecting cracks and taking predictive maintenance-based decisions by combining historical data, real-time sensor data, and predictive models. The proposed DT-based framework provides insights into the potential crack formation, allowing for proactive maintenance and mitigation measures. We compare the performance of our proposed network on different evaluation measures such as precision, recall, overall accuracy, mean average precision, F1-score, and kappa coefficient, where our proposed lightweight multiscale feature fusion-based network outperformed all other state-of-the-art deep neural networks. We also achieved the best performance on mean average precision by surpassing all other models. Additionally, we also compared the performance of our proposed network with U-Net and recurrent neural network on model training and prediction time benchmarks by outperforming those cutting-edge models.

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Atrous spatial pyramid convolution for object detection with encoder-decoder

Cited by 20 publications

References 16 publications

A multi-scale spatial–temporal capsule network based on sequence encoding for bearing fault diagnosis

A multi-scale spatial–temporal capsule network based on sequence encoding for bearing fault diagnosis

AENet: attention enhancement network for industrial defect detection in complex and sensitive scenarios

Deep learning and IoT enabled digital twin framework for monitoring open-pit coal mines

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