The traditional vehicle type recognition algorithm has a low image recognition rate for various vehicle types on diverse road conditions and is prone to being affected by shooting distance, light intensity, and weathe...
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The traditional vehicle type recognition algorithm has a low image recognition rate for various vehicle types on diverse road conditions and is prone to being affected by shooting distance, light intensity, and weather. To address these problems, a new separate convolutional neural network structure was proposed to automatically classify the images of different vehicle types based on the deep learning tensorflow framework and the classical GoogLeNet-based network model. Experimental results on the data sets of BIT-Vehicle and Cars-196 show that, compared with the traditional HOG_BP algorithm and convolutional neural network model, the decomposed convolutional neural network has a higher recognition rate for the same difficult vehicle images, and its average accuracy rate reaches 96.30%. In addition, the adjustment of hyperparameters in the network ensures that the parameters, such as weight and bias amount, are more efficient and reasonable when constantly updated.
Stock market is enormous and tough to comprehend. Because of the market's high volatility, it is regarded as being uncertain and unpredictable. Investing in a good stock at the wrong moment can be fatal, whereas i...
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A gated branch neural network (GBNN) is proposed for modelling mandatory lane changing (MLC) behaviour at the on-ramps of highways. It provides a core algorithm for an MLC suggestion system for advanced driver assista...
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A gated branch neural network (GBNN) is proposed for modelling mandatory lane changing (MLC) behaviour at the on-ramps of highways. It provides a core algorithm for an MLC suggestion system for advanced driver assistance systems (ADAS), where the main challenge is the trade-off between computational speed and prediction accuracy for both non-merge and merge events. The GBNN algorithm employs a gated branch based on correlation analysis, scaled exponential linear units activation function, and adaptive moment estimation optimiser. The algorithm has been evaluated using the real-world dataset of U.S. Highway 101 and Interstate 80 from Federal Highway Administration's Next Generation Simulation (NGSIM). Input features are extracted from NGSIM and pre-processed by standardisation and principal component analysis. tensorflow framework and Python are used as the development platform. Results show that the proposed GBNN algorithm with the Pearson correlation method has values of 97.7%, 96.3%, and 0.990 for non-merge accuracy, merge accuracy, and receiver operating characteristic score, respectively. It outperforms other traditional binary classifiers for MLC applications, and is more light-weight than a convolutional neural network (AlexNet) of deep learning algorithm. Owing to its compact architecture, the GBNN provides high accuracy and efficiency, demonstrating promising usage as an MLC suggestion system in ADAS.
This work addresses the problem of intra-class classification of Breast Histopathology images into Eight (8) classes of either Benign or Malignant Cell. Current manual features extraction and classification is fraught...
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ISBN:
(纸本)9781728101323
This work addresses the problem of intra-class classification of Breast Histopathology images into Eight (8) classes of either Benign or Malignant Cell. Current manual features extraction and classification is fraught with inaccuracies leading to high rate false negatives with attendant mortality. Deep Convolutional Neural Networks (DCNN) have been shown to be effective in classification of Images. We adopted a DCNN architecture combined with Ensemble learning method using tensorflow framework with Backpropagation training and ReLU activation function to achieve accurate automated classification of these Images. We achieved inter-class classification accuracy of 91.5% with the BreakHis dataset.
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