Aiming at the problem that traditional visible light imaging systems are easily affected by environmental factors and cannot accurately describe the scene information, a gradient residual generative adversarial network for polarization image fusion is proposed in this paper. In the generator network structure, dense blocks are added to the encoder part to retain more features, the gradient residual module is constructed for fusing the feature maps to enhance the texture and detail features of the image, and the multi-scale weighted structural similarity loss function and gradient loss function are designed to improve the network performance. The experimental results show that the method in this paper obtains fused images with richer texture structure and more in line with the visual sense perception of the human eye, and at the same time has the optimal objective evaluation index.
KEYWORDS: Education and training, Convolution, Neural networks, Detection and tracking algorithms, Data modeling, RGB color model, Performance modeling, Feature extraction, Deep learning, Neurons
In recent years, research on distracted driving behavior recognition has made significant progress, with an increasing number of researchers focusing on deep-learning-based algorithms. Aiming at the problems of the existing distracted driving recognition algorithm, such as its oversized model and difficulty in adapting to low computing environments, a lightweight network MobileNetV2, is chosen as the backbone network and improved to design a distracted driving behavior detection method that is both accurate and practical. The Ghost module is employed to replace point-by-point convolution to reduce the computation, the Leaky ReLU function helps mitigate the problem of dead neurons, as it prevents gradients from becoming zero for negative inputs. Finally, the channel pruning algorithm is used to further reduce the model parameters. The experiment results on the State Farm dataset show that the model’s test accuracy can reach 94.66%, and the number of parameters is only 0.23 M. The improved model has significantly fewer parameters than the baseline model, which demonstrates the effectiveness and applicability of the method.
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