Faster training of Mask R-CNN by focusing on instance boundaries

Abstract

We present an auxiliary task to Mask R-CNN, an instance segmentation network, which leads to faster training of the mask head. Our addition to Mask R-CNN is a new prediction head, the Edge Agreement Head, which is inspired by the way human annotators perform instance segmentation. Human annotators copy the contour of an object instance and only indirectly the occupied instance area. Hence, the edges of instance masks are particularly useful as they characterize the instance well. The Edge Agreement Head therefore encourages predicted masks to have similar image gradients to the ground-truth mask using edge detection filters. We provide a detailed survey of loss combinations and show improvements on the MS COCO Mask metrics compared to using no additional loss. Our approach marginally increases the model size and adds no additional trainable model variables. While the computational costs are increased slightly, the increment is negligible considering the high computational cost of the Mask R-CNN architecture. As the additional network head is only relevant during training, inference speed remains unchanged compared to Mask R-CNN. In a default Mask R-CNN setup, we achieve a training speed-up and a relative overall improvement of 8.1% on the MS COCO metrics compared to the baseline.

Tree Cover Estimation in Global Drylands from Space Using Deep Learning

“…Many studies show how deep-learning classification is a powerful tool for land cover and crop types using remote sensing data [53,54], detection of individual trees in RGB images [55], identification of tree species related to forest disturbance with very high resolution multispectral images [56], and detection of firs damaged by bark beetle in unmanned aerial vehicle (UAV) images [57]. However, the studies to detect the percentage of land use surface, is addressed by segmentation methods such as Mask-RCNN [58] with high computational cost [59]. In this study, tree cover estimation is approached from a simple classification method [42] with better results than manual methods [19].…”

Convolutional Neural Networks–Based Image Analysis for the Detection and Quantification of Neutrophil Extracellular Traps

“…For our work, we chose an open source implementation by Matterport [34] in Python with the use of Keras and Tensorflow frameworks. The model and its implementation has already gained popularity among various researchers [35][36][37][38][39][40] for of several reasons-The model is published under MIT license, which allows users to modify the model; it adopts well established CNN backbone ResNet [41] and recently introduced concepts like Feature Pyramid Network (FPN) [42] and ROI Align that in terms of quality make Mask R-CNN superior to comparable models like Faster R-CNN; the maximum accepted input image resolution of the model (1024 × 1024 pixels) is high when compared to many previously developed CNN models like YOLO (up to 608 × 608 pixels) [43] or Faster R-CNN [42] Python implementation (600 × 1000 pixels). The ability to analyze higher resolution images is important especially when dealing with small objects like biological cells [35].…”

A Crop Image Segmentation and Extraction Algorithm Based on Mask RCNN

“…In order to further improve the accuracy of the segmentation mask, a method of adding edge loss [ 22 ] to the mask branch is proposed to make the edge of the segmentation result more accurate. First, the labeled image is converted into a binary segmentation map of the crop, which is the target mask, and then the prediction mask and the target mask output by the mask branch are used as input, and they are convolved with the Sobel operator [ 23 ].…”