{'fraction_non_alphanumeric': 0.027746319365798414, 'fraction_numerical': 0.009060022650056626, 'mean_word_length': 5.243816254416961, '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}}
A novel bottom-up keypoints based hexagon bolts detection method

Hexagon bolts are common standard parts in manufacturing industry and generally accompanied with anti-loosing components like lockwire and locking tab, to strengthen fastening effect. However, incorrect assembly will cause failure, even accelerate bolt loosening. Consequently, misassembly inspection is necessary. For an automatic inspection system based on computer vision, the localization information of hexagon bolts centers and corners is needed. Object detection and keypoints detection technologies develop rapidly, and some researches have applied these technologies to hexagon bolts and their keypoints detection. These existing methods use the top-down strategy, which need two separate models for each stage. This strategy is common and mature, but still has some shortcomings. First, detection models suffer from the imbalance between positive and negative samples. Second, the quality of bounding box detection will affect keypoints detection. Third, two separate models cause computation redundancy. To overcome these problems, we propose a novel bottom-up keypoints based hexagon bolts detection method. With the small dataset, relatively high-resolution images and limited computation resources, our method achieves a good balance between speed and accuracy. We build two hexagon bolts datasets, one contains locking tab with large field of view (LFOV), another contains lockwire with small field of view (SFOV). Images with large field of view have more complex background. We test our model on both two datasets, and the results show that our method is effective. For the SFOV, our model reaches 0.950 AP and 0.974 recall. For the LFOV, our model reaches 0.883 AP and 0.934 recall.