Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The m...Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.展开更多
文摘Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.
文摘海洋动物是南极气候变化的＂生物指示剂＂,其排泄物中丰富的碳（C）和氮（N）等营养物质为土壤中温室气体的产生与排放提供了有利条件,企鹅作为一种重要的海洋动物,因此其聚居区成为甲烷（CH4）和氧化亚氮（N2O）等温室气体排放的潜在＂热点＂区域.然而,受企鹅数量遥感资料的限制,区域尺度上企鹅源温室气体排放总量尚缺乏精确估算.以南极维多利亚地难言岛企鹅聚集区为研究对象,基于0.1 m分辨率航拍照片发展了面向像元的RGB颜色模型法（pixel-oriented RGB color model）识别企鹅数量,通过企鹅粪便CH4和N2O排放通量、企鹅排便量等数据建立了企鹅源温室气体估算模型.结果显示,航拍照片中企鹅像元在RGB彩色空间模型中的R值（17~104）与其他背景像元（〉110）存在显著差异,该差异可以作为将企鹅与背景像元有效分离的理论依据;南极维多利亚地难言岛企鹅总数为19150只,企鹅源CH4和N2O排放总量分别约为275和2.99 kg.
