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• W4386283969 abstract "Road detection technology is an important part of the automatic driving environment perception system. With the development of technology, the situations that automatic driving needs to consider will become broader and more complex. This paper contributes a lightweight convolutional neural network model, incorporating novel convolution and parallel pooling modules, an improved network activation function, and comprehensive training and verification with multiple datasets. The proposed model achieves high accuracy in detecting drivable areas in complex autonomous driving situations while significantly improving real-time performance. In addition, we collect data in the field and create small datasets as reference datasets for testing algorithms. This paper designs relevant experimental scenarios based on the datasets and experimental platforms and conducts simulations and real-world vehicle experiments to verify the effectiveness and stability of the algorithm models and technical solutions. The method achieves an MIoU of 90.19 and a single batch time of 340 ms with a batch size of 8, which substantially reduces the runtime relative to a typical deep network structure like ResNet50." @default.
• W4386283969 created "2023-08-31" @default.
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• W4386283969 date "2023-08-30" @default.
• W4386283969 modified "2023-09-30" @default.
• W4386283969 title "Drivable Area Detection in Unstructured Environments based on Lightweight Convolutional Neural Network for Autonomous Driving Car" @default.
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• W4386283969 doi "https://doi.org/10.3390/app13179801" @default.
• W4386283969 hasPublicationYear "2023" @default.
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