Based on computer vision technology, this paper proposes a method for identifying and locating crops in order to successfully capture crops in the process of automatic crop picking. This method innovatively combines the YOLOv3 algorithm under the DarkNet framework with the point cloud image coordinate matching method, and can achieve the goal of this paper very well. Firstly, RGB (RGB is the color representing the three channels of red, green and blue) images and depth images are obtained by using the Kinect v2 depth camera. Secondly, the YOLOv3 algorithm is used to identify the various types of target crops in the RGB images, and the feature points of the target crops are determined. Finally, the 3D coordinates of the feature points are displayed on the point cloud images. Compared with other methods, this method of crop identification has high accuracy and small positioning error, which lays a good foundation for the subsequent harvesting of crops using mechanical arms. In summary, the method used in this paper can be considered effective.

We present a novel adaptive multimodal intensity-event algorithm to optimize an overall objective of object tracking under bit rate constraints for a host-chip architecture. The chip is a computationally resource-constrained device acquiring high-resolution intensity frames and events, while the host is capable of performing computationally expensive tasks. We develop a joint intensity-neuromorphic event rate-distortion compression framework with a quadtree (QT)-based compression of intensity and events scheme. The goal of this compression framework is to optimally allocate bits to the intensity frames and neuromorphic events based on the minimum distortion at a given communication channel capacity. The data acquisition on the chip is driven by the presence of objects of interest in the scene as detected by an object detector. The most informative intensity and event data are communicated to the host under rate constraints so that the best possible tracking performance is obtained. The detection and tracking of objects in the scene are done on the distorted data at the host. Intensity and events are jointly used in a fusion framework to enhance the quality of the distorted images, in order to improve the object detection and tracking performance. The performance assessment of the overall system is done in terms of the multiple object tracking accuracy (MOTA) score. Compared with using intensity modality only, there is an improvement in MOTA using both these modalities in different scenarios.

This paper proposes a novel sparsity adaptive simulated annealing algorithm to solve the issue of sparse recovery. This algorithm combines the advantage of the sparsity adaptive matching pursuit (SAMP) algorithm and the simulated annealing method in global searching for the recovery of the sparse signal. First, we calculate the sparsity and the initial support collection as the initial search points of the proposed optimization algorithm by using the idea of SAMP. Then, we design a two-cycle reconstruction method to find the support sets efficiently and accurately by updating the optimization direction. Finally, we take advantage of the sparsity adaptive simulated annealing algorithm in global optimization to guide the sparse reconstruction. The proposed sparsity adaptive greedy pursuit model has a simple geometric structure, it can get the global optimal solution, and it is better than the greedy algorithm in terms of recovery quality. Our experimental results validate that the proposed algorithm outperforms existing state-of-the-art sparse reconstruction algorithms.

This study primarily investigates image sensing at low sampling rates with convolutional neural networks (CNN) for specific applications. To improve the image acquisition efficiency in energy-limited systems, this study, inspired by compressed sensing, proposes a fully learnable model for task-driven image-compressed sensing (FLCS). The FLCS, based on Deep Convolution Generative Adversarial Networks (DCGAN) and Variational Auto-encoder (VAE), divides the image-compressed sensing model into three learnable parts, i.e., the Sampler, the Solver and the Rebuilder. To be specific, a measurement matrix suitable for a type of image is obtained by training the Sampler. The Solver calculates the image’s low-dimensional representation with the measurements. The Rebuilder learns a mapping from the low-dimensional latent space to the image space. All the mentioned could be trained jointly or individually for a range of application scenarios. The pre-trained FLCS reconstructs images with few iterations for task-driven compressed sensing. As indicated from the experimental results, compared with existing approaches, the proposed method could significantly improve the reconstructed images’ quality while decreasing the running time. This study is of great significance for the application of image-compressed sensing at low sampling rates.

The present study primarily investigates the topic of image reconstruction at high compression rates. As proven from compressed sensing theory, an appropriate algorithm is capable of reconstructing natural images from a few measurements since they are sparse in several transform domains (e.g., Discrete Cosine Transform and Wavelet Transform). To enhance the quality of reconstructed images in specific applications, this paper builds a trainable deep compressed sensing model, termed as EnGe-CSNet, by combining Convolution Generative Adversarial Networks and a Variational Autoencoder. Given the significant structural similarity between a certain type of natural images collected with image sensors, deep convolutional networks are pre-trained on images that are set to learn the low dimensional manifolds of high dimensional images. The generative network is employed as the prior information, and it is used to reconstruct images from compressed measurements. As revealed from the experimental results, the proposed model exhibits a better performance than competitive algorithms at high compression rates. Furthermore, as indicated by several reconstructed samples of noisy images, the model here is robust to pattern noise. The present study is critical to facilitating the application of image compressed sensing.

As traffic congestion becomes more and more serious and the harm of traffic safety to humans is increasing, many traffic managers and researchers try to find ways to solve traffic problems from various aspects. In order to truly reflect the driver's car following mechanism in the road, a car-following model based on the social force (Social Force Car-following Model, SFCFM) is proposed. In this paper, we use the NGSIM data to calibrate and evaluate the model based on the social force following model and the classical GM car following model. The evaluation results show that the simulation accuracy of acceleration, velocity and position of the new model is improved by 0.18, 0.92 and 1.04m compared with the traditional GM model. The new model is more in line with the actual situation than the traditional GM model, which can provide a reference for the analysis of the car following behavior.