Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

Algorithm 1

Improved Faster R-CNN process.

(1)	Input image A, adjust the image size, and output image B with a specified size M × N.
(2)	Using B as the input of the feature extraction module, obtain a multilevel fused feature map C via DenseNet.
(3)	Using C as the input of RPN, obtain 300 proposals: D by the sliding window method. RPN changes the generated anchors through box regression so that they can be closer to the marker boxes.
(4)	Using C and D as the inputs of RoI, obtain a mapping map E that is between the proposal and the feature map.
(5)	Output E separately to the classifier and the regressor. The classifier achieves classification and identification of E using Softmax, while the regressor further corrects the boxes by Soft-NMS regression. Finally, classify and localize the objects.