{'fraction_non_alphanumeric': 0.02975206611570248, 'fraction_numerical': 0.00909090909090909, 'mean_word_length': 5.024875621890548, '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}}
Multiscale Hierarchical Attention Fusion Network for Edge Detection

Edge detection is one of the basic challenges in the field of computer vision. The results of most existing methods now produce thick edges and background interference. The images generated by the network must be post-processed with non-maximum suppression. To tackle these problems, in this paper we proposes a novel edge detection model, which allows the network to focus on learning the contextual features of the image, thereby obtaining more accurate pixel edges. First, we use the multi-scale feature layering module in the model to obtain richer image multi-granularity features in the high-level features. Secondly, after the multi-scale feature layering module, we use the channel attention in the edge attention module and use the spatial attention for low-level features. and finally through a fusion layer to fuse the output of channel attention and spatial attention. Evaluate our method on two benchmark popular datasets, BSDS500[1] and NYUDv2[2], and achieved the best accuracy, 78.1%, 73% that has not been processed using non-maximum suppression (NMS). We apply out method to water ripple detection and get good performance.