An Accurate Method Based on Deep Learning Architectures for Real-world Object Detection

Li-juan YANG, Xiang PAN, Jia-chi ZHANG, Hong ZHOU


Object detection and recognition play an important role in blind navigation. However, in real-world shooting, images are degraded and deviate from the statistical distribution of academic datasets, which have a bad impact on object detection. Here, using deep neural networks and door detection as an example, we simulate problems that the blind may encounter when shooting images, such as lack of illumination in the imaging environment, relative motion with the object in exposure moment, rotation and jitter of the photographic apparatus and the like. After establishing a mathematical model of image degradation, we compare and demonstrate the impact of various image degradation on door detection. By degenerating the training set, we train a robust model that improves the average precision (AP) of the door detection in real scenes and outperforms other training methods.


Deep learning, Object detection, Model training, Image processing


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