人工智能
目标检测
计算机科学
过程(计算)
领域(数学)
计算机视觉
分割
像素
卷积神经网络
交叉口(航空)
RGB颜色模型
算法
实时计算
工程类
数学
纯数学
航空航天工程
操作系统
作者
Zhu Jia-jun,Michihisa Iida,Sikai Chen,Shinko Y. Cheng,Masahiko Suguri,R. Masuda
摘要
Abstract The development of robotic combine for rice harvesting has garnered worldwide attention in recent years. The robotic combine is capable of running along a designated path; however, it still requires human operator supervision due to the lack of object detection sensors for safety purposes. To achieve a fully unmanned robotic combine, a real‐time paddy field object detection method is necessary. Typically, all paddy field objects are detected individually using multiple algorithms and sensors, which significantly increases the complexity and cost of the detection process. In this study, the deep learning (DL) based semantic segmentation (SS) method was employed to detect all paddy field objects simultaneously using only an RGB camera. Considering the environment of the paddy field, a new SS model called “The Robotic Combine Network (TRCNet)” was specifically designed for the robotic combine. And four state‐of‐the‐art lightweight convolutional neural networks were applied as the backbones of the TRCNet. To achieve real‐time detection, TensorRT (NVIDIA) was utilized for speeding up the prediction process. All models were trained and evaluated using paddy field images captured during the robotic combine's harvesting process. The results showed that the TRCNet can successfully detect all paddy field objects. The mean intersection over union, and frames per second (FPS) of the best two SS models were 0.823, 47.48, and 0.834, 32.44, respectively. The FPS values were obtained after speed acceleration and tested with an image size of 640 × 480 pixels on an embedded processor (Jetson TX2), enabling real‐time object detection in paddy fields for the robotic combine.
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