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An algorithm for automatic identification of multiple developmental stages of rice spikes based on improved faster r-cnn

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The Crop Journal 2022年第5期10卷 1323-1333页

作者： Yuanqin Zhang Deqin Xiao Youfu Liu Huilin Wu College of Mathematics and Informatics South China Agricultural UniversityGuangzhou 510642GuangdongChina Guangzhou National Modern Agricultural Industry Science and Technology Innovation Center Guangzhou 511458GuangdongChina

Spike development directly affects the yield and quality of rice. We describe an algorithm for automatically identifying multiple developmental stages of rice spikes(AI-MDSrS) that transforms the automatic identification of multiple developmental stages of rice spikes into the detection of rice spikes of diverse maturity levels. The scales vary greatly in different growth and development stages because rice spikes are dense and small, posing challenges for their effective and accurate detection. We describe a rice spike detection model based on an improved faster regions with convolutional neural network(faster r-cnn).The model incorporates the following optimization strategies: first, Inception_res Net-v2 replaces VGG16 as a feature extraction network;second, a feature pyramid network(FPN) replaces single-scale feature maps to fuse with region proposal network(rPN);third, region of interest(ro I) alignment replaces ro I pooling, and distance-intersection over union(DIo U) is used as a standard for non-maximum suppression(NMS). The performance of the proposed model was compared with that of the original faster r-cnn and YOLOv4 models. The mean average precision(m AP) of the rice spike detection model was92.47%, a substantial improvement on the original faster r-cnn model(with 40.96% m AP) and 3.4%higher than that of the YOLOv4 model, experimentally indicating that the model is more accurate and reliable. The identification results of the model for the heading–flowering, milky maturity, and full maturity stages were within two days of the results of manual observation, fully meeting the needs of agricultural activities.

关键词： improved faster r-cnn rice spike detection rice spike count Developmental stage identification

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Computer-Assisted Detection of Colonic Polyps Using improved faster r-cnn

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Chinese Journal of Electronics 2019年第4期28卷 718-724页

作者： LI Jiangyun ZHANG Jie CHANG Dedan HU Yaojun School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Department of Gastroenterology Fu Xing Hospital Capital Medical University

The deficiencies of existing polyp detection methods remain: i) They primarily depend on the manually extracted features and require considerable amounts of preprocessing. ii) Most traditional methods cannot specify the location of the polyps in colonoscopy images, especially for the polyps with variable size. In order to derive the improvement and lift the accuracy, we propose a novel and scalable detection algorithm based on deep neural networks—an improved faster regionbased Convolutional neural networks(faster r-cnn)—by increasing the fusion of feature maps at different *** can be employed to detect and locate polyps, and even achieve a multi-object task for polyps in the *** experimental consequences demonstrate that the best version among improved algorithms achieves 97.13%accuracy on the CVC-ClinicDB database, overtaking the previous methods.

关键词： Colonic polyps Deep learning improved faster r-cnn Object detection

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