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Swift, non-destructive detection approaches should address the problem of insufficient sensitivity when attempting to obtain and perceive live crab information in low-light environments caused by the crab's phototaxis. We propose a learning-based low-illumination image enhancer (LigED) for effective enhanced lighting and elimination of darkness in images. The camera response function was combined with the reflectance ground-truth mechanism of image decomposition. Self-attention units were then introduced in the reflectance restoration network to adjust the illumination to avoid visual defects, thus jointly strengthening the adaptability of dark-light enhancement and ability to perceive crab information. Convolutional neural network (CNN)-based detection methods can further enhance the algorithm's robustness to light and adaptability to different environments, which motivated the development of a scalable lightweight live crab detector (EfficientNet-Det0) utilizing the two-stage compound scaling CNN approach. The lightness order error and natural image quality evaluator based on the proposed methods were 251.26 and 11.60, respectively. The quality of average precision detection increased by 13.84–95.40%. The fastest detection speed of a single image was 91.74/28.41 f·s−1 using a common GPU/CPU, requiring only 15.1 MB of storage, which advocates for the utilization of LigED and EfficientNet-Det0 for the efficient detection of underwater live crabs. [ABSTRACT FROM AUTHOR]

The recognition of apple fruits in plastic bags is easy to be affected by reflected and refracted light. In order to weaken the influence of light, a method based on block classification is proposed. The method adopts watershed algorithm to segment original images into irregular blocks based on edge detection results of R-G grayscale images firstly. Compared with the watershed algorithm based on gradient images, the segmentation method can preserve fruits edge and reduce the number of blocks by 20.31%, because graying image method, R-G, filters most of leaves and edge detection operator insures that the edge of fruits are detected accurately. Next, these blocks are classified into fruit blocks and non-fruit blocks by support vector machine on the basis of the color and texture features extracted from blocks. Compared with the image recognition method based on pixel classification, the proposed method can restrain the interference of light caused by plastic bags effectively. The false negative rate (FNR) and false positive rate (FPR) of the method based on pixel classification are 21.71 and 14.53% respectively. The FNR and FPR of the proposed method are 4.65 and 3.50% respectively. [ABSTRACT FROM AUTHOR]

In this work, potassium-containing Cu2SnS3 thin films (KxCu2−xSnS3) were fabricated by a simple and low-cost ultrasonic spray pyrolysis method on glass substrates with different potassium doping concentration. Effects of various potassium concentrations on the structural, morphological and optical properties of the films were systematically investigated. The results indicate that potassium doping can well enhance the growth of particle size and decrease the band gap of the film from to 1.55 to 1.26 eV. Solar cell devices with a structure of glass/FTO/K2−xCu2SnS3/CdS/ZnO/AZO have been prepared and obvious solar cell device performance improvement has been observed by 2 at% potassium doping. This potassium-containing Cu2SnS3 thin films could be a promising absorber material for high efficiency solar cell. [ABSTRACT FROM AUTHOR]

In order to improve the image denoising ability, the wavelet transform (WT) and independent component analysis (ICA) are both introduced into image denoising in this paper. Although these two algorithms have their own advantages in image denoising, they are unable to reduce noises completely, which makes it difficult to achieve ideal effect. Therefore, a new image denoising method is proposed based on the combination of WT with ICA (WT-ICA). For verifying the WT-ICA denoising method, we adopt four image denoising methods for comparison: median filtering (MF), wavelet soft thresholding (WST), ICA, and WT-ICA. From the experimental results, it is shown that WT-ICA can significantly reduce noises and get lower-noise image. Moreover, the average of WT-ICA denoising image’s peak signal to noise ratio (PSNR) is improved by 20.54% compared with noisy image and 11.68% compared with the classical WST denoising image, which demonstrates its advantage. From the performance of texture and edge detection, denoising image by WT-ICA is closer to the original image. Therefore, the new method has its unique advantage in image denoising, which lays a solid foundation for the realization of further image processing task. [ABSTRACT FROM AUTHOR]

As apple harvesting needs large amount of labor, and the seasonality is strong, the night operation of apple harvesting robot is proposed, in order to improve the efficiency of harvesting. The apple's real-time night vision image contains lots of noise, which is captured by image processing system of apple harvesting robot. The noise will influence the operating efficiency and recognition precision, and then influence the harvesting efficiency. Under different artificial lights, apple night vision images are captured, the noises are analyzed through the difference image method, and the type of noise is determined to be mixed noise. The main part of mixed noise is Gaussian noise, accompanied by some salt-pepper noise. Aiming at the problem of Gaussian noise removal, the theory of independent component analysis (ICA) is introduced into the de-noising method for night vision image. The ICA algorithm mostly uses gradient iterative solver, so it has some defects, such as easily trapped in local minimum, slow convergence speed. All of these defects lead to the following phenomena easily, such as the unthoroughness in the de-noising and the long running time. In order to overcome these defects, particle swarm optimization (PSO) algorithm is used to optimize the ICA, further to establish an optimized ICA de-noising method based on PSO (PSO-ICA), applied in night vision image, hoping to minimize noise pollution and improving the operating efficiency of de-noising method. Using the standard Lenna image and apple image captured under nature light, by the simulation experiments, these 2 pictures are added with the Gaussian noise with the variance of 0.05 and the salt-pepper noise with the P value of 0.05, respectively. Compared with the average filtering method and ICA de-noising method, the results show that the de-noising effect of PSO-ICA algorithm is the most ideal. Using peak signal-to-noise ratio (PSNR) to do difference test, the result shows that, under 0.05 significant level, 3 de-noising methods show significant difference. Using different apple night vision images captured to do experiments, the results show that, from the visual evaluation, the low noise image is obtained by PSO-ICA de-noising method, and its noise decreased significantly. In order to evaluate the de-noising effect of night vision image more objectively, taking the natural light image as reference, the concept of relative peak signal-to-noise ratio (RPSNR) is proposed. From the RPSNR evaluation, compared with the original image, the image after average filtering de-noising and that after ICA de-noising, the image based on the method of PSO-ICA de-noising increased on average by 21.28%, 12.41% and 5.53%, respectively. From the run time evaluation, PSO algorithm has greatly improved the efficiency of ICA algorithm. Under incandescent lamp, the night vision image and its de-noised images have the highest RPSNR, so this type of light is suitable for artificial light source. Finally, under the natural light and 3 different artificial lights, 10 images of natural light and 30 night images are captured from 10 sample points. Using all of these images to do the repeated experiments, the trends of experimental results are consistent. In conclusion, PSO-ICA algorithm has unique advantage for night vision image de-noising, which provides a solid foundation for the night operation of apple picking robot. [ABSTRACT FROM AUTHOR]

For yield measurement of an apple orchard or the mechanical harvesting of apples, there needs to be accurate identification of the target apple fruit. However, in a natural scene, affected by the apple's growth posture and camera position, there are many kinds of apple images, such as overlapped apples; mutual shadows or leaves; stems; etc. It is a challenge to accurately locate overlapped apples. They will influence the positioning time and recognition efficiency and then affect the harvesting efficiency of apple-harvesting robots or the accuracy of orchard yield measurement. In response to this problem, an overlapped circle positioning method based on local maxima is proposed. First, the apple image is transformed into the Lab color space and segmented by the K-means algorithm. Second, some morphological processes, like erosion and dilation, are implemented to abstract the outline of the apples. Then image points are divided into central points; edge points; or outer points. Third, a fast algorithm is used to calculate every internal point's minimum distance from the edge. Then, the centers of the apples are obtained by finding the maxima among these distances. Last, the radii are acquired by figuring out the minimum distance between the center and the edge. Thus, positioning is achieved. Experimental results showed that this method can locate overlapped apples accurately and quickly when the apple contour was complete; and this has certain practicability. [ABSTRACT FROM AUTHOR]

Chinese river crabs are important aquatic products in China, and the accurate operation of aquatic plants cleaning workboat is an urgent need for solving various problems in the aquaculture process. In order to achieve the accurate navigation positioning, this article introduces the visual-aided navigation system and combines the advantages of particle filter in nonlinear and non-Gaussian systems. Meanwhile, the generalized regression neural network is used to adjust the particle weights so that the samples are closer to the posterior density, thus avoiding the phenomenon of particle degradation and keeping the diversity of particles. In order to improve the network performance, the fruit fly optimization algorithm is introduced to adjust the smoothing factor of transfer function for the generalized regression neural network model layer. On this basis, the location filtering navigation method based on fruit fly optimization algorithm-generalized regression neural network-particle filter is proposed. According to the simulation results, the meanR of root-mean-square error of the proposed fruit fly optimization algorithm-generalized regression neural network- particle filter method decreases by 12.39% and 6.87%, respectively, compared with those of particle filter and generalized regression neural network methods, and the meanT of running time decreases by 16.04% and 9.14%, respectively. From the repeated experiments on the aquatic plants cleaning workboat in crab ponds, the latitude error of the proposed method decreases by 23.45% and 12.68%, respectively, and that the longitude error decreases by 29.11% and 17.65%, respectively, compared with those of particle filter and generalized regression neural network methods. It is proved that our proposed method can effectively improve the navigation positioning accuracy of aquatic plants cleaning workboat. [ABSTRACT FROM AUTHOR]