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 identificat...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.展开更多
The characteristics of chicken droppings are closely related to the health of chickens.Veterinarians often judge the health of a chicken by looking at whether the chicken poop is normal.At present,the inspection of ab...The characteristics of chicken droppings are closely related to the health of chickens.Veterinarians often judge the health of a chicken by looking at whether the chicken poop is normal.At present,the inspection of abnormal chicken droppings in chicken coops relies on manual observation,which is inefficient,accurate varies from person to person,labor-intensive,and has the risk of cross-infection.To achieve efficient,accurate,and intelligent identification of abnormal chicken droppings,an abnormal chicken droppings detection method based on improved Faster Region-based Convolutional Neural Network(Faster R-CNN)was proposed in this study.In the feature extraction network stage,deformable convolution was used and combined with Path Augmentation-Feature Pyramid Network(PA-FPN)to improve the extraction ability of features at different scales.In the Region Proposal Network(RPN)stage,the K-means++algorithm was used to cluster the dataset and obtain the Anchor-ratio which is more suitable for the chicken poop object,and the FocalLoss classification loss function was used to improve the classification ability of difficult samples.In the regional convolutional network stage,the region of interest calibration algorithm was used instead to obtain more accurate localization information.The experimental results show that the improved Faster R-CNN structure can reach an accuracy of 98.8%for abnormal chicken poop detection,and the average accuracy mean value was improved by 27.8%.The results can provide a key core technology support for establishing an efficient abnormal chicken droppings online detection system.展开更多
基金supported by the Key-Area Research and Development Program of Guangdong Province (2019B020214005)Agricultural Research Project and Agricultural Technology Promotion Project of Guangdong (2021KJ383)。
文摘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.
基金supported by the 'Zhu Cheng Professorial Workstation of China Agricultural University',and the Science and Technology Cooperation Project between China and the United Kingdom under the National Key Research and Development Program(Envirobot an Autonomous Roving Platform for Environment,Health,and Welfare Monitoring of Poultry(Grant No.2017YFE0122200).
文摘The characteristics of chicken droppings are closely related to the health of chickens.Veterinarians often judge the health of a chicken by looking at whether the chicken poop is normal.At present,the inspection of abnormal chicken droppings in chicken coops relies on manual observation,which is inefficient,accurate varies from person to person,labor-intensive,and has the risk of cross-infection.To achieve efficient,accurate,and intelligent identification of abnormal chicken droppings,an abnormal chicken droppings detection method based on improved Faster Region-based Convolutional Neural Network(Faster R-CNN)was proposed in this study.In the feature extraction network stage,deformable convolution was used and combined with Path Augmentation-Feature Pyramid Network(PA-FPN)to improve the extraction ability of features at different scales.In the Region Proposal Network(RPN)stage,the K-means++algorithm was used to cluster the dataset and obtain the Anchor-ratio which is more suitable for the chicken poop object,and the FocalLoss classification loss function was used to improve the classification ability of difficult samples.In the regional convolutional network stage,the region of interest calibration algorithm was used instead to obtain more accurate localization information.The experimental results show that the improved Faster R-CNN structure can reach an accuracy of 98.8%for abnormal chicken poop detection,and the average accuracy mean value was improved by 27.8%.The results can provide a key core technology support for establishing an efficient abnormal chicken droppings online detection system.
文摘热轧带钢是钢铁行业的重要产品,其表面缺陷是影响产品质量的重要因素。针对传统缺陷检测算法存在的过程繁琐、精度不足和效率低下等问题,提出一种基于改进更快速区域卷积神经网络(faster region-based convolutional neural network,Faster R-CNN)的检测算法,实现对热轧带钢表面缺陷的高效、高精度检测。首先,采用特征相加的方法对底层细节特征和高层语义特征进行融合;然后,采用精准的感兴趣区域池化(precise region of interest pooling,Precise ROI Pooling)获取固定大小的特征向量,避免特征出现位置偏差;最后,利用均值偏移聚类算法对带钢数据集进行聚类,获得适用于热轧带钢表面缺陷检测的先验框尺寸。实验结果表明,所提算法在热轧带钢表面缺陷检测数据集上的平均精度均值达到了85.34%,检测速度为23.5帧/s,且鲁棒性良好,满足实际的工业检测需求。
