{'fraction_non_alphanumeric': 0.018583042973286876, 'fraction_numerical': 0.0, 'mean_word_length': 4.863945578231292, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Obstacle Detection Using GoogleNet

Obstacle detection is one of the important parts of systems such as navigation systems or self-driving cars. Most of the proposed approaches for obstacle detection are based on special sensors which are expensive and (or) hard to use. In this article, a new method is introduced which is based on Deep Neural Networks (DNN) and detects obstacle by using a single camera. This method consists of an unsupervised DNNs to extract global features of image and a supervised one to extract local features of image (block). The proposed method uses the advantages of some neighborhood coefficients to consider the impact of the neighboring blocks during local feature extraction (which would be done by supervised CNN). The focus of this article is on the obstacle detection while this approach could be used in depth inference too.