Using depth information and colour space variations for improving outdoor robustness for instance segmentation of cabbage

2021-03-31 · Nils Lüling, David Reiser, Alexander Stana, H. W. Griepentrog

Image-based yield detection in agriculture could raiseharvest efficiency and cultivation performance of farms. Following this goal, this research focuses on improving instance segmentation of field crops under varying environmental conditions. Five data sets of cabbage plants were recorded under varying lighting outdoor conditions. The images were acquired using a commercial mono camera. Additionally, depth information was generated out of the image stream with Structure-from-Motion (SfM). A Mask R-CNN was used to detect and segment the cabbage heads. The influence of depth information and different colour space representations were analysed. The results showed that depth combined with colour information leads to a segmentation accuracy increase of 7.1%. By describing colour information by colour spaces using light and saturation information combined with depth information, additional segmentation improvements of 16.5% could be reached. The CIELAB colour space combined with a depth information layer showed the best results achieving a mean average precision of 75.

📄 PDF Abstract BibTeX arXiv:2103.16923

Code (0)

등록된 구현이 없습니다.

Tasks

Instance Segmentation Segmentation Semantic Segmentation

Methods 이 논문이 사용한 방법론

RPN A Region Proposal Network, or RPN, is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained…

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

RoIAlign Region of Interest Align, or RoIAlign, is an operation for extracting a small feature map from each RoI in detection and segmentation based tasks. It removes the harsh…

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

Mask R-CNN Mask R-CNN extends Faster R-CNN to solve instance segmentation tasks. It achieves this by adding a branch for predicting an…

Recolour What Matters: Region-Aware Colour Editing via Token-Level Diffusion

2026-03-19 · Yuqi Yang, Dongliang Chang, Yijia Ling, Ruoyi Du 외 arxiv

Colour is one of the most perceptually salient yet least controllable attributes in image generation. Although recent diffusion models can modify object colours from user instructions, their results often deviate from th…

Image Generation

EdgeNet: Semantic Scene Completion from a Single RGB-D Image

2019-08-08 · Aloisio Dourado, Teofilo Emidio de Campos, Hansung Kim, Adrian Hilton

Semantic scene completion is the task of predicting a complete 3D representation of volumetric occupancy with corresponding semantic labels for a scene from a single point of view. Previous works on Semantic Scene Comple…

3D Semantic Scene Completion Edge Detection

Efficient Depth Completion Using Learned Bases

2020-12-02 · Yiran Zhong, Yuchao Dai, Hongdong Li

In this paper, we propose a new global geometry constraint for depth completion. By assuming depth maps often lay on low dimensional subspaces, a dense depth map can be approximated by a weighted sum of full-resolution p…

Depth Completion

ScaleDepth: Decomposing Metric Depth Estimation into Scale Prediction and Relative Depth Estimation

2024-07-11 · Ruijie Zhu, Chuxin Wang, Ziyang Song, Li Liu 외

Estimating depth from a single image is a challenging visual task. Compared to relative depth estimation, metric depth estimation attracts more attention due to its practical physical significance and critical applicatio…

Depth Estimation Monocular Depth Estimation

Semantic Scene Completion Combining Colour and Depth: preliminary experiments

2018-02-13 · Andre Bernardes Soares Guedes, Teofilo Emidio de Campos, Adrian Hilton

Semantic scene completion is the task of producing a complete 3D voxel representation of volumetric occupancy with semantic labels for a scene from a single-view observation. We built upon the recent work of Song et al. …

3D Semantic Scene Completion