To address the problem of low accuracy of traditional scene recognition methods in indoor environment, an indoor scene recognition method combining multi-scale features and attention mechanism is proposed. The method uses Efficientnet-B3 as the backbone network, introduces channel and spatial attention modules to improve the refinement capability of the network for features, and designs a multi-scale feature fusion structure to enhance the adaptability of the network to scales, based on which the model parameters are optimized by adding a spatial pyramid, thus further improving the model calculation accuracy. The experimental analysis shows that the average accuracy of this model reaches 94.4% in nine types of indoor scenes, all of which are better than the calculation results of AlexNet, VGGNet16, GoogLeNet, ResNet34, EfficientNet and other models, providing a new way of thinking for indoor scene recognition.
In order to solve the problem of semantic segmentation difficulties of city street scene pictures due to uneven color and strong light changes, this paper proposes a semantic segmentation algorithm of city street scene pictures based on DeepLabV3+ architecture with dark channel a priori theory. The algorithm firstly inverts the city streetscape image and then performs color balancing through the dark channel a priori module, then extracts contextual information hierarchically using a multi-channel parallel network, fuses low-level features with high-level features hierarchically to obtain the optimized feature map, secondly completes the fusion of multi-scale features through the spatial pyramid structure, and finally fuses the obtained feature map with the decoder twice to generate the final prediction results. The experimental results show that the algorithm in this paper outperforms the original DeeplabV3+ model in terms of subjective perception and objective indicators in urban scenes, and provides a new solution and technical idea for semantic segmentation of urban scenes.
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