Three-Dimensional Target Detection Based on RGB-D Data

The current three-dimensional (3D) target detection model has a low accuracy, because the surface information of the target can only be partially represented by its two-dimensional (2D) image detector. To solve the problem, this paper studies the 3D target detection in the RGB-D data of indoor scenes, and modifies the frustum PointNet (F-PointNet), a model superior in point cloud data processing, to detect indoor targets like sofa, chair, and bed. The 2D image detector of F-PointNet was replaced with you only look once (YOLO) v3 and faster region-based convolutional neural network (R-CNN) respectively. Then, the F-PointNet models with the two 2D image detectors were compared on SUN RGB-D dataset. The results show that the model with YOLO v3 did better in target detection, with a clear advantage in mean average precision (>6.27).

A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network

Increasing grain production is essential to those areas where food is scarce. Increasing grain production by controlling crop diseases and pests in time should be effective. To construct video detection system for plant diseases and pests, and to build a real-time crop diseases and pests video detection system in the future, a deep learning-based video detection architecture with a custom backbone was proposed for detecting plant diseases and pests in videos. We first transformed the video into still frame, then sent the frame to the still-image detector for detection, and finally synthesized the frames into video. In the still-image detector, we used faster-RCNN as the framework. We used image-training models to detect relatively blurry videos. Additionally, a set of video-based evaluation metrics based on a machine learning classifier was proposed, which reflected the quality of video detection effectively in the experiments. Experiments showed that our system with the custom backbone was more suitable for detection of the untrained rice videos than VGG16, ResNet-50, ResNet-101 backbone system and YOLOv3 with our experimental environment.