Gravelly soil uniformity identification based on the optimized Mask R-CNN model

Qu, Xin-Ping; Wang, Jiajun; Wang, Xiaoling; Hu, Yusheng; Zeng, Tuocheng; Tan, Tianwen

doi:10.1016/j.eswa.2022.118837

Cited by 8 publications

(2 citation statements)

References 45 publications

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“…Although these types of structures have not yet been applied to the classification of drug-resistant organisms, they have been utilized in a variety of applications. ResNet101 successfully solved the detection of gravelly soil uniformity, and it was superior to all other architectures [ 55 ]. MobileNetV2 is a small, low-latency, low-power model parameterized to satisfy the resource restrictions of a range of use cases.…”

Section: Related Workmentioning

confidence: 99%

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

et al. 2022

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A person infected with drug-resistant tuberculosis (DR-TB) is the one who does not respond to typical TB treatment. DR-TB necessitates a longer treatment period and a more difficult treatment protocol. In addition, it can spread and infect individuals in the same manner as regular TB, despite the fact that early detection of DR-TB could reduce the cost and length of TB treatment. This study provided a fast and effective classification scheme for the four subtypes of TB: Drug-sensitive tuberculosis (DS-TB), drug-resistant tuberculosis (DR-TB), multidrug-resistant tuberculosis (MDR-TB), and extensively drug-resistant tuberculosis (XDR-TB). The drug response classification system (DRCS) has been developed as a classification tool for DR-TB subtypes. As a classification method, ensemble deep learning (EDL) with two types of image preprocessing methods, four convolutional neural network (CNN) architectures, and three decision fusion methods have been created. Later, the model developed by EDL will be included in the dialog-based object query system (DBOQS), in order to enable the use of DRCS as the classification tool for DR-TB in assisting medical professionals with diagnosing DR-TB. EDL yields an improvement of 1.17–43.43% over the existing methods for classifying DR-TB, while compared with classic deep learning, it generates 31.25% more accuracy. DRCS was able to increase accuracy to 95.8% and user trust to 95.1%, and after thae trial period, 99.70% of users were interested in continuing the utilization of the system as a supportive diagnostic tool.

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Section: Related Workmentioning

confidence: 99%

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

et al. 2022

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“…To further verify the segmentation performance of the proposed model, other mainstream instance segmentation models, i.e., Mask R-CNN [34,35] and YOLACT [36], were selected and used in contrast experiments with LPSS-YOLOv5. Specifically, the same data sets were used for the comparison, and the results are shown in Table 6.…”

Section: Performance Comparison Of the Mainstream Instance Segmentati...mentioning

confidence: 99%

Instance Segmentation of Lotus Pods and Stalks in Unstructured Planting Environment Based on Improved YOLOv5

Chen

et al. 2023

Agriculture

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Accurate segmentation of lotus pods and stalks with pose variability is a prerequisite for realizing the robotic harvesting of lotus pods. However, the complex growth environment of lotus pods causes great difficulties in conducting the above task. In this study, an instance segmentation model, LPSS-YOLOv5, for lotus pods and stalks based on the latest YOLOv5 v7.0 instance segmentation model was proposed. The CBAM attention mechanism was integrated into the network to improve the model’s feature extraction ability. The scale distribution of the multi-scale feature layer was adjusted, a 160 × 160 small-scale detection layer was added, and the original 20 × 20 large-scale detection layer was removed, which improved the model’s segmentation accuracy for small-scale lotus stalks and reduced the model size. On the medium-large scale test set, LPSS-YOLOv5 achieved a mask mAP0.5 of 99.3% for all classes. On the small-scale test set, the mAP0.5 for all classes and AP0.5 for stalks were 88.8% and 83.3%, which were 2.6% and 5.0% higher than the baseline, respectively. Compared with the mainstream Mask R-CNN and YOLACT models, LPSS-YOLOv5 showed a much higher segmentation accuracy, speed, and smaller size. The 2D and 3D localization tests verified that LPSS-YOLOv5 could effectively support the picking point localization and the pod–stalk affiliation confirmation.

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