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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Ford Motorcar Identification from Single-Camera Side-View Image Based on Convolutional Neural Network
verfasst von : Shui-Hua Wang, Wen-Juan Jia, Yu-Dong Zhang
Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2017
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Abstract
Aim: This study proposed an application of convolutional neural network (CNN) on vehicle identification of Ford motorcar. We used single camera to obtain vehicle images from side view. Method: We collected a 100-image dataset, among which 50 were Ford motorcars and 50 were non-Ford motorcars. We used data augmentation to enlarge its size to 3900-image. Then, we developed an eight-layer CNN, which was trained by stochastic gradient descent with momentum method. Results: Our CNN method achieves a sensitivity of 93.64%, a specificity of 93.13, and an accuracy of 93.38%. Conclusion: This proposed CNN method performs better than three state-of-the-art approaches.