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Gesture recognition nowadays has grabbed the attention of researchers as they represent human behaviour in multiple practical ways. Amongst a variety of gestures available, hand gestures play an essential role in the field of human‐computer interaction when recognised efficiently in complex and dynamic environments. In this paper, we propose a dynamic hand gesture recognition system to recognise hand gestures appearing in different indoor and outdoor environments. Hand detection and tracking uses a two‐level system resulting in the formation of gesture trajectory in challenging conditions in which existing detection and tracking algorithms could not do so. A set of 45 features is provided as input to the various classification techniques. The redundancy problem has been reduced by selecting a set of optimum features using the analysis of variance method, which ranks the list of features. An incremental feature selection technique calculates recognition accuracy by selecting features according to rankings. This system provides an accuracy of 96.32% when used with machine learning and 97.5% when used with deep learning techniques. Recognition accuracy is calculated for various environments, including an extra hand, multiple persons in the video frame, and outdoor environment. All machine‐learning classifiers are combined using classifier combination to calculate the accuracy according to the majority‐voting rule. Based on the experimental results, it has been observed that deep learning provides better results compared to machine learning. [ABSTRACT FROM AUTHOR]

Automatic fruit recognition is one of the most important steps in fruit picking robots. In this study, a novel fruit recognition was proposed using improved YOLOv3, in order to identify the fruit quickly and accurately for the picking robot in the complex environment of orchard (different light, occlusion, adhesion, large field of view, bagging, whether the fruit was mature or not). The specific procedure was as follows. 1) 4000 Apple images were captured under the complex environment via the orchard shooting and Internet collection. After labeling with LabelImg software, 3200 images were randomly selected as training set, 400 as verification set, and 400 as test set. Mosaic data enhancement was also embedded in the model to improve the input images for the better generalization ability and robustness of model. 2) The network model was also improved. First, the residual module in DarkNet53 network was combined with the CSPNet to reduce the amount of network calculation, while maintaining the detection accuracy. Second, the SPP module was added to the detection network of the original YOLOv3 model, further to fuse the global and local characteristics of fruits, in order to enhance the recall rate of model to the minimal fruit target. Third, a soft NMS was used to replace the traditional for better recognition ability of model, particularly for the overlapping fruits. Forth, the joint loss function using Focal and CIoU Loss was used to optimize the model for a higher accuracy of recognition. 3) The model was finally trained in the deep learning environment of a server, thereby to analyze the training process after the dataset production and network construction. Optimal weights and parameters were achieved, according to the loss curve and various performance indexes of verification set. The results show that the best performance was achieved, when training to the 109th epoch, where the obtained weight in this round was taken as the final model weight, precision was 94.1%, recall was 90.6%, F1 was 92.3%, mean average precision was 96.1%. Then, the test set is used to test the optimal model. The experimental results show that the Mean Average Precision value reached 96.3%, which is higher than 92.5% of the original model; F1 value reached 91.8%, higher than 88% of the original model; The average detection speed of video stream under GPU is 27.8 frame/s, which is higher than 22.2 frame/s of the original model. Furthermore, it was found that the best comprehensive performance was achieved to verify the effectiveness of the improved, compared with four advanced detection of Faster RCNN, RetinaNet, YOLOv5 and CenterNet. A comparison experiment was conducted under different fruit numbers and various lighting environments, further to verify the effectiveness and feasibility of the improved model. Correspondingly, the detection performance of model was significantly better for small target apples and severely occluded overlapping apples, compared with the improved YOLOv3 model, indicating the high effectiveness. In addition, the target detection using deep learning was robust to illumination, where the illumination change presented little impact on the detection performance. Consequently, the excellent detection, robustness and real-time performance can widely be expected to serve as an important support for accurate fruit recognition in complex environment. [ABSTRACT FROM AUTHOR]

• A novel method using deep learning-based amplification and tracking is proposed to measure the micro-vibration. • This method can be used under complex light fields with different illumination and backgrounds. • The target object is first magnified and tracked, and then converted into actual displacement values. A new deep learning-based vision measurement method is proposed to accurately measure the micro-vibration displacements of objects in different illuminations and backgrounds. The measurement method pre-process the video, then the deep learning correlation methods are used to zoom in the target object and track the vibration trajectory, and the pixel displacement is converted to actual displacement by pixel equivalents. By comparing the three sets of experiments, the proposed method has exceptional accuracy. When measuring vibration displacement of 0.1 mm, the Root Mean Square Error (RMSE) and Normalized Root Mean Square Error (NRMSE) are 0.0234 mm and 11.8601 %. By comparing with the Rectangle Detection Algorithm and the Template Matching Algorithm, the proposed algorithm outperforms these two traditional methods, especially for the complex environments. It can be concluded that this method, as a new visual measurement method, can be adapted to a variety of complex environments and can accurately measure micro-amplitude vibrations. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the network models of traditional intelligent single-pixel imaging methods have poor suppression ability in complex environments (e.g., smoke, fog, haze, etc.), a single-pixel imaging method based on random discarding mechanism (DSPINet) is proposed. The method integrates compressed sensing and deep learning. It compresses and samples the image directly, discards part of the information, and reconstructs the image with a small number of the one-dimensional detection signal. In view of the fact that most of the data of traditional single-pixel imaging methods are obtained in a controlled environment and do not have good generalization ability, this paper introduces the random discarding mechanism into the network model to simulate the light scattering loss and system noise in the complex environment. The test results show that when the simulation test's sampling rate is 10.00 %, the proposed DSPINet model is compared with the DLGI (improved), DLBGI, Bsr2-Net, and TVAL3, the structural similarity (SSIM) of reconstructed images is improved by 2.20 %, 4.49 %, 5.68 %, and 63.16 %, respectively. In the actual test of introducing the scattering medium, when the sampling rate is only 3.00–10.00 %, the reconstructed images generated by the DSPINet model have a good ability to suppress the interference and show a high reconstruction accuracy in the complex environment. [ABSTRACT FROM AUTHOR]