多模块融合的浮游生物检测器.

To solve the problems of detection process with low detection accuracy and redundancy in the traditional detection method of Marine plankton by artificial features extraction, a multi module fusion single shot detector ( MMFSSD) was proposed based on deep learning technology, The feature information enhancement module was proposed to add the receptive field of network without increasing the network complexity, and the down-sampled image was infused into the module to enhance the low-level feature information of feature graph, The selective feature fusion module was further proposed to learn the weight of fusion in the network and selectively fuse features of different scales, The results of verification test show that the mean average precision values are 80, 70% and 32, 20% on PASCAL VOC and MS COCO test-set, respectively, The mean average precision on PMID2019 data-set reaches 90, 41 %. [ABSTRACT FROM AUTHOR]