Enhanced imaging through turbid water based on quadrature lock-in discrimination and retinex aided by adaptive gamma function for illumination correction

This paper presents an improved method for imaging in turbid water by using the individual strengths of the quadrature lock-in discrimination(QLD)method and the retinex method.At first,the high-speed QLD is performed on images,aiming at capturing the ballistic photons.Then,we perform the retinex image enhancement on the QLD-processed images to enhance the contrast of the image.Next,the effect of uneven illumination is suppressed by using the bilateral gamma function for adaptive illumination correction.The experimental results depict that the proposed approach achieves better enhancement than the existing approaches,even in a high-turbidity environment.

Automatic detection of pecan fruits based on Faster RCNN with FPN in orchard

Although the development of the robot picking vision system is widely applied,it is very challenging for fruit detection in orchards with complex light and environment,especially for fruit colors similar to the background.In recent,there are few studies on pecan fruit detection and location based on machine vision.In this study,an accurate and efficient pecan fruit detection method was proposed based on machine vision under natural pecan orchards.In order to solve the illumination problem,a light compensation algorithm was first utilized to process the collected samples,and then an improved Faster Region Convolutional Neural Network(Faster RCNN)with the Feature Pyramid Networks(FPN)was established to train the samples.Finally,the pecan number counting method was introduced to count the number of pecan.A total of 241 pecan images were tested,and comparison experiments were carried out.The mean average precision(mAP)of the proposed detection method was 95.932%,compared with the result without uneven illumination correction(UIC),which was increased by 0.849%,while the mAP of the Single Shot Detector(SSD)+FPN was 92.991%.In addition,the number of clusters was counted using the proposed method with an accuracy rate of 93.539%compared with the actual clusters.The results demonstrate that the proposed network has good robustness for pecan fruit detection in different illumination and various unstructured environments,and the experimental achievement has great potential for robot-picking visual systems.

Image enhancement for crop trait information acquisition system

Collecting images using portable devices is an effective and convenient method for acquiring crop trait information.Because of uncertain environmental conditions in the field,enhancement is necessary to improve the visual quality of images.With this aim,here we propose an adaptive image enhancement method based on guided filtering.Our method automatically calculates the enhancement weights of the detail in an image according to the distribution characteristics of the illumination intensity of a crop image,so as to adaptively adjust the contrast of the image.To verify the effectiveness of the proposed algorithm,we performed enhancement experiments on 50 images of four kinds of cucumber leaf tissues,namely,leaves infected with target spot,powdery mildew,and downy mildew,and healthy leaves.The results showed that our proposed method substantially improved the visual quality of the images.Moreover,the mean ratios of the contrast to color difference obtained using the proposed method were higher than the mean ratios obtained using five conventional enhancement methods.We consider the proposed method for image enhancement will be a valuable addition to the crop trait information acquisition system(http://ebreed.com.cn/).