Inland Navigation Mark detection Algorithm based on Improved Cascade RCNN
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Graphical Abstract
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Abstract
The algorithm is developed to answer the request of intelligent navigation for technologies for detection, identification and classification of navigation marks. The feature extraction network, anchor box mechanism, detection window suppression algorithm and loss function of typical faster RCNN are improved and the structure of the cascade RCNN is modified by introduction of ResNext, soft-NMS and GIoU (generalized intersection over union). The algorithm is verified with the data of navigation Marks in Wuhan waterway. Experiments show that the algorithm achieved average accuracy of 94.17% with the process speed of 190 ms per frame. This algorithm is seen strong in dealing with overlapping small targets.
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