Instance Segmentation and Classification Method for Plant Leaf Images Based on ISC-MRCNN and APS-DCCNN

Abstract

To solve the complex background problems (e.g. Noise interference, object overlap, and different illumination) that affect the classification performance on plant leaf images, this paper proposes an instance segmentation and classification method for plant leaf images based on IFPN SNMS CFFI-Mask R-CNN (ISC-MRCNN) and ACPSOSVM-Dual Channels Convolutional Neural Network (APS-DCCNN). To obtain the foreground of plant leaf images, the lateral connection structure of the feature map pyramid in ISC-MRCNN fuses the feature maps of different depths, so that the network learns more detailed features. Then, the Soft Non-Maximum Suppression Algorithm is employed to improve the detection performance of overlapping objects. Next, the pooling method of integrating the continuous function can reduce the precision loss during the alignment of the mapping between the feature map and the original image. Finally, by constructing a mask filter layer, complex backgrounds are masked. To distinguish the similarity between plant leaf images, APS-DCCNN is used to classify the foreground images. In this process, the Support Vector Machine is used to replace softmax and then an Adaptive Chaotic Particle Swarm Algorithm is employed to optimize it. The experimental results show that compared with Mask R-CNN, the average precision of ISC-MRCNN has increased by 1.89% under different thresholds. The proposed method is suitable for the object detection and instance segmentation problems with complex background. Besides, compared with traditional CNN, the average precision of the classification results obtained by APS-DCCNN has improved by 1.59%. This has shown that the proposed method is suitable for the classification of plant leaves.