Analysis and Design of Surgical Instrument Localization Algorithm

With the help of surgical navigation system,doctors can operate on patients more intuitively and accurately.The positioning accuracy and real-time performance of surgical instruments are very important to the whole system.In this paper,we analyze and design the detection algorithm of surgical instrument location mark,and estimate the posture of surgical instrument.In addition,we optimized the pose by remapping.Finally,the algorithm of location mark detection proposed in this paper and the posture analysis data of surgical instruments are verified and analyzed through experiments.The final result shows a high accuracy.

Spectral sensitivity of ALOS, ASTER, IKONOS, LANDSAT and SPOT satellite imagery intended for the detection of archaeological crop marks

This study compares the spectral sensitivity of remotely sensed satellite images,used for the detection of archaeological remains.This comparison was based on the relative spectral response(RSR)Filters of each sensor.Spectral signatures profiles were obtained using the GER-1500 field spectroradiometer under clear sky conditions for eight different targets.These field spectral signature curves were simulated to ALOS,ASTER,IKONOS,Landsat 7-ETM-,Landsat 4-TM,Landsat 5-TM and SPOT 5.Red and near infrared(NIR)bandwidth reflectance were re-calculated to each one of these sensors using appropriate RSR Filters.Moreover,the normalised difference vegetation index(NDVI)and simple ratio(SR)vegetation profiles were analysed in order to evaluate their sensitivity to sensors spectral filters.The results have shown that IKONOS RSR filters can better distinguish buried archaeological remains as a result of difference in healthy and stress vegetation(approximately 18%difference in reflectance of the red and NIR band and nearly 0.07 to the NDVI profile).In comparison,all the other sensors showed similar results and sensitivities.This difference of IKONOS sensor might be a result of its spectral characteristics(bandwidths and RSR filters)since they are different from the rest of sensors compared in this study.

Semantic Segmentation-Based Road Marking Detection Using Around View Monitoring System

Road marking detection is an important branch in autonomous driving,understanding the road information.In recent years,deep-learning-based semantic segmentation methods for road marking detection have been arising since they can generalize detection result well under complicated environments and hold rich pixel-level semantic information.Nevertheless,the previous methods mostly study the training process of the segmentation network,while omitting the time cost of manually annotating pixel-level data.Besides,the pixel-level semantic segmentation results need to be fitted into more reliable and compact models so that geometrical information of road markings can be explicitly obtained.In order to tackle the above problems,this paper describes a semantic segmentation-based road marking detection method using around view monitoring system.A semiautomatic semantic annotation platform is developed,which exploits an auxiliary segmentation graph to speed up the annotation process while guaranteeing the annotation accuracy.A segmentation-based detection module is also described,which models the semantic segmentation results for the more robust and compact analysis.The proposed detection module is composed of three parts:vote-based segmentation fusion filtering,graph-based road marking clustering,and road-marking fitting.Experiments under various scenarios show that the semantic segmentation-based detection method can achieve accurate,robust,and real-time detection performance.

RAIENet:End-to-End Multitasking Road All Information Extractor

Road lanes and markings are the bases for autonomous driving environment perception.In this paper,we propose an end-to-end multi-task network,Road All Information Extractor named RAIENet,which aims to extract the full information of the road surface including road lanes,road markings and their correspondences.Based on the prior knowledge of pavement information,we explore and use the deep progressive relationship between lane segmentation and pavement mark-ing detection.Then,different attention mechanisms are adapted for different tasks.A lane detection accuracy of 0.807 F1-score and a ground marking accuracy of 0.971 mean average precision at intersection over union(IOU)threshold 0.5 were achieved on the newly labeled see more on road plus(CeyMo+)dataset.Of course,we also validated it on two well-known datasets Berkeley Deep-Drive 100K(BDD100K)and CULane.In addition,a post-processing method for generating bird’s eye view lane(BEVLane)using lidar point cloud information is proposed,which is used for the construction of high-definition maps and subsequent decision-making planning.The code and data are available at https://github.com/mayberpf/RAIEnet.