KEYWORDS: Convolution, Detection and tracking algorithms, Object detection, Video, Education and training, Image segmentation, Video acceleration, Video processing, Feature extraction, Data modeling
The existing unsafe driving behavior detection algorithms are difficult to meet the requirements of good real-time performance and high precision. This paper proposes an improved GhostNet unsafe driving behavior detection algorithm. The algorithm uses the Ghost convolution operator to reduce the complexity of the algorithm and improve the real-time performance. In addition, a dual-scale time adaptive module (DCTAM) is designed to extract the temporal information. Through fusing spatial and temporal information to improve the accuracy of detecting unsafe driving behaviors. The experimental results show that the algorithm in this paper reduces the computational complexity and stabilizes the detection accuracy.
Semantic segmentation is a key step of image prehension. Single use of convolutional networks for semantic segmentation makes it difficult to distinguish the same class of objects with large contour deviations, while the higherlevel features will lose some the detailed information. Currently, Networks such as ACFNet and DANet have introduced attention mechanism to improve scene classification by obtaining rich contextual information through self-controlled system, but they do not combine both global scope and class feature relationships in local space to further advance intraclass consistency and inter-class divisibility of features. In terms of this problem, semantic segmentation network of categorical attention with spatial constraints has been proposed, which contains two submodules, one using the category spatial distribution to introduce local spatial location information of features, and the other using the global category average strength to introduce global strength information of category features. By selecting a kind of appropriate backbone network, this network model obtains the feature map from the backbone network and stacks the features into the original features after two submodules of global category strength and category local space processing, finally, performing classification processing by the classification layer and up-samples to the input image size to complete the pixel-level label prediction. The experiment result demonstrates that this proposed segmentation network has higher accuracy than existing segmentation networks.
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