Detection of skin defects on loquat using hyperspectral imaging combining both band radio and improved three-phase level set segmentation method

Background and objectives Skin defects are one of the primary problems that occur in post-harvest grading and processing of loquats.Skin defects lead to the loquat being easily destroyed during transportation and storage,which causes the risk of other loquats being infected,affecting the selling price.Materials and Methods In this paper,a method combining band radio image with an improved three-phase level set segmentation algorithm(ITPLSSM)is proposed to achieve high accuracy,rapid,and non-destructive detection of skin defects of loquats.Principal component analysis(PCA)was used to find the characteristic wavelength and PC images to distinguish four types of skin defects.The best band ratio image based on characteristic wavelength was determined.Results The band ratio image(Q782/944)based on PC2 image is the best segmented image.Based on pseudo-color image enhancement,morphological processing,and local clustering criteria,the band ratio image(Q782/944)has better contrast between defective and normal areas in loquat.Finally,the ITPLSSM was used to segment the processing band ratio image(Q782/944),with an accuracy of 95.28%.Conclusions The proposed ITPLSSM method is effective in distinguishing four types of skin defects.Meanwhile,it also effectively segments images with intensity inhomogeneities.

Three-dimensional inversion of knot defects recognition in timber cutting

The comprehensive utilization of wood is the main goal of log cutting,but knot defects increase the diffi-culty of rationally optimizing cutting.Due to the lack of real shape data of knot defects in logs,it is diffi cult for detection methods to establish a correlation between signal and defect morphology.An image-processing method is proposed for knot inversion based on distance regularized level set segmentation(DRLSE)and spatial vertex clustering,and with the inversion of the defects existing relative board position in the log,an inversion model of the knot defect is established.First,the defect edges of the top and bottom images of the boards are extracted by DRLSE and ellipse fi tting,and the major axes of the ellipses made coplanar by angle correction;second,the coordinate points of the top and bottom ellipse edges are extracted to form a spatial straight line;third,to solve the intersection dispersion of spatial straight lines and the major axis plane,K-medoids clustering is used to locate the vertex.Finally,with the vertex and the large ellipse,a 3D cone model is constructed which can be used to invert the shape of knots in the board.The experiment was conducted on ten defective larch boards,and the experimental results showed that this method can accurately invert the shapes of defects in solid wood boards with the advantages of low cost and easy operation.

Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore satisfy the true demands of night scene target detection applications.We very much believe that our method will help future research.