Dataset Information

A comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles.

ABSTRACT: Unmanned ground vehicles (UGVs) are an important research application of artificial intelligence. In particular, the deep learning-based object detection method is widely used in UGV-based environmental perception. Good experimental results are achieved by the deep learning-based object detection method Faster region-based convolutional neural network (Faster R-CNN). However, the exploration space of the region proposal network (RPN) is restricted by its expression. In our paper, a boosted RPN (BRPN) with three improvements is developed to solve this problem. First, a novel enhanced pooling network is designed in this paper. Therefore, the BRPN can adapt to objects with different shapes. Second, the expression of BRPN loss function is improved to learn the negative samples. Furthermore, the grey wolf optimizer (GWO) is used to optimize the parameters of the improved BRPN loss function. Thereafter, the performance of the BRPN loss function is promoted. Third, a novel GA-SVM classifier is applied to strengthen the classification capacity. The PASCAL VOC 2007, VOC 2012 and KITTI datasets are used to test the BRPN. Consequently, excellent experimental results are obtained by our deep learning-based object detection method.

SUBMITTER: Xu Q

PROVIDER: S-EPMC8118342 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundThe identification and enumeration of medicinal plants at high elevations is an important part of accurate yield calculations. However, the current assessment of medicinal plant reserves continues to rely on field sampling surveys, which are cumbersome and time-consuming. Recently, unmanned aerial vehicle (UAV) remote sensing and deep learning (DL) have provided ultrahigh-resolution imagery and high-accuracy object recognition techniques, respectively, providing an excellent opportunity to improve the current manual surveying of plants. However, accurate segmentation of individual plants from drone images remains a significant challenge due to the large variation in size, geometry, and distribution of medicinal plants.ResultsIn this study, we proposed a new pipeline for wild medicinal plant detection and yield assessment based on UAV and DL that was specifically designed for detecting wild medicinal plants in an orthomosaic. We used a drone to collect panoramic images of Lamioplomis rotata Kudo (LR) in high-altitude areas. Then, we annotated and cropped these images into equally sized sub-images and used a DL model Mask R-CNN for object detection and segmentation of LR. Finally, on the basis of the segmentation results, we accurately counted the number and yield of LRs. The results showed that the Mask R-CNN model based on the ResNet-101 backbone network was superior to ResNet-50 in all evaluation indicators. The average identification precision of LR by Mask R-CNN based on the ResNet-101 backbone network was 89.34%, while that of ResNet-50 was 88.32%. The cross-validation results showed that the average accuracy of ResNet-101 was 78.73%, while that of ResNet-50 was 71.25%. According to the orthomosaic, the average number and yield of LR in the two sample sites were 19,376 plants and 57.93 kg and 19,129 plants and 73.5 kg respectively.ConclusionsThe combination of DL and UAV remote sensing reveals significant promise in medicinal plant detection, counting, and yield prediction, which will benefit the monitoring of their populations for conservation assessment and management, among other applications.

Dataset Information

A comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets