Fine-grained remote sensing semantic segmentation method under multi-stage supervision learning

Wang, Rui; Qiu, Kaijian

doi:10.1109/icmtma54903.2022.00075

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…With the development of Convolutional neural network (CNN), the emergence of semantic segmentation model based on depth Neural Network has met many industrial background needs, such as building crack detection [1], remote sensing information recognition [2], automatic medical image analysis [3], etc. However, unlike the process of target detection data analysis in general industrial tasks, the background of automatic driving application is more inclined to real-time scene understanding for advanced driving task planning.…”

Section: Introductionmentioning

confidence: 99%

Research on fast semantic segmentation algorithm of road potholes under rain and snow

Zhao

Jiao²,

Ye³

et al. 2023

Second International Conference on Digital Society and Intelligent Systems (DSInS 2022)

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To meet the speed and accuracy requirements of road semantics segmentation algorithm scenarios, a lightweight semantics segmentation model, MADNet, based on MobileNetV2, is presented, which effectively reduces the computational load of convolution neural network. The feature enhancement module uses a pooled pyramid of empty space convolution. In the deep and shallow part of the MADNet network, attention mechanism is added to compensate for the decline in feature extraction accuracy of MobileNetV2. Finally, the data enhancement algorithm is used to train the identification task in rain and snow weather, road depression and automobile dataset scenarios. The results of ablation test and algorithm comparison test verify that the algorithm proposed in this paper can achieve a better effect and faster speed for road depression and fast semantic segmentation of vehicles in rain and snow weather.

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Section: Introductionmentioning

confidence: 99%