Detection of the foreign object positions in agricultural soils using Mask-RCNN

Objects in agricultural soils will seriously affect the farming operations of agricultural machinery.At present,it still relies on human experience to judge abnormal Gounrd-penetrting Radar(GPR)signals.It is difficult for traditional image processing technology to form a general positioning method for the randomness and diversity characteristics of GPR signals in soil.Although many scholars had researched a variety of image-processing techniques,most methods lack robustness.In this study,the deep learning algorithm Mask Region-based Convolutional Neural Network(Mask-RCNN)and a geometric model were combined to improve the GPR positioning accuracy.First,a soil stratification experiment was set to classify the physical parameters of the soil and study the attenuation law of electromagnetic waves.Secondly,a SOIL-GPR geometric model was proposed,which can be combined with Mask-RCNN's MASK geometric size to predict object sizes.The results proved the effectiveness and accuracy of the model for position detection and evaluation of objects in soils;then,the improved Mask RCNN method was used to compare the feature extraction accuracy of U-Net and Fully Convolutional Networks(FCN);Finally,the operating speed of agricultural machinery was simulated and designed the A-B survey line experiment.The detection accuracy was evaluated by several indicators,such as the survey line direction,soil depth false alarm rate,Mean Average Precision(mAP),and Intersection over Union(IoU).The results showed that pixel-level segmentation and positioning based on Mask RCNN can improve the accuracy of the position detection of objects in agricultural soil effectively,and the average error of depth prediction is 2.87 cm.The results showed that the detection technology proposed in this study integrates the advantage of soil environmental parameters,geometric models,and artificial intelligence algorithms to provide a high-precision and technical solution for the GPR non-destructive detection of soils.

A review on foreign object detection for magnetic coupling-based electric vehicle wireless charging

With the rapid development and widespread application of electric vehicles(EVs)around the world,the wireless power transfer(WPT)technology is also accelerating for commercial applications in EV wireless charging(EV-WPT)because of its high reliability,safety,and convenience,especially high suitability for the future self-driving scenario.Foreign object detection(FOD),mainly including metal object detection and living object detection,is required urgently and timely for the practical application of EV-WPT technology to ensure electromagnetic safety.In the last decade,especially in the past three years,many pieces of research on FOD have been reported.This article reviews FOD state-of-the-art technology for EV-WPT and compares the pros and cons of different approaches in terms of sensitivity,reliability,adaptability,complexity,and cost.Future challenges for research and development are also discussed to encourage commercialisation of EV-WPT technique.

Rail Detection Based on LSD and the Least Square Curve Fitting

It is necessary to rely on the rail gauge to determine whether the object beside the track will affect train operation safety or not.A convenient and fast method based on line segment detector(LSD)and the least square curve fitting to identify the rail in the image is proposed in this paper.The image in front of the train can be obtained through the camera on-board.After preprocessing,it will be divided equally along the longitudinal axis.Utilizing the characteristics of the LSD algorithm,the edges are approximated into multiple line segments.After screening the terminals of the line segments,it can generate the mathematical model of the rail in the image based on the least square.Experiments show that the algorithm in this paper can fit the rail curve accurately and has good applicability and robustness.