At present, there are two main problems with fruit-and vegetable-picking robots. One is that complex scenes (with backlighting, direct sunlight, overlapping fruit and branches, blocking leaves, etc.) obviously interfere with the detection of fruits and vegetables; the other is that an embedded platform needs a lighter detection method due to performance constraints. To address these problems, a fast tomato detection method based on improved YOLOv3-tiny is proposed. First, we improve the precision of the model by improving the backbone network; second, we use image enhancement to improve the detection ability of the algorithm in complex scenes. Finally, we design several groups of comparative experiments to prove the rationality and feasibility of this method. The experimental results show that the f1-score of the tomato recognition model proposed in this paper is 91.92%, which is 12% higher than that of YOLOv3-tiny; the detection speed on a CPU can reach 25 frames/s, and the inferential speed is 40.35 ms, equivalent to that of YOLOv3-tiny. Through comparative experiments, we can see that the comprehensive performance of our method is better than that of other state-of-the-art methods.

Presently, to obtain a more accurate density map and crowd number, existing methods often count by combining training RGB images and depth images. However, these methods are not ideal for capturing and fusing complementary features in RGB-D. Therefore, to solve the above problems, we propose a collaborative cross-modal attention network named CCANet for accurate RGB-D crowd counting. CCANet is mainly composed of the collaborative cross-modal attention module (CCAM) and the collaborative cross-modal fusion module (CCFM). Specifically, CCAM focuses on adaptive, interleaved RGB-D information through channel and spatial cross-modal attentions to fully capture complementary features in different modes. CCFM can adaptively integrate these features by weighing the importance of the above complementary features. A large number of experiments on the ShanghaiTechRGBD and MICC benchmarks have proven the effectiveness of CCANet in RGB-D crowd counting. In addition, our CCANet is generally applicable to multimodal crowd counting and has achieved superior counting performance on the RGBT-CC benchmark.

Crowd counting is a task of intelligent applications, and its operation efficiency is very important. However, in order to obtain a better counting performance, most of the existing works often design larger and more complex network structures, which will result in them occupying considerable memory, time and other resources at runtime, seriously limiting their deployment scope and making it difficult to be widely used in practical scenarios. In this paper, in order to overcome the above problems, we propose an effective lightweight encoding-decoding crowd counting network, named Lw-Count. Specifically, in the encoding process, we design an efficient and lightweight convolution module (ELCM), which extracts the crowd features in the network through a refined ghost block to reduce the network parameters and computing cost, and solves the problem of inaccurate counting caused by uneven crowd distribution through spatial group normalization (SGN). In the decoding process, we design a scale regression module (SRM) to reduce the error details and chessboard effect caused by linear interpolation and deconvolution. In addition, we design a new loss function, which enhances the spatial correlation of the density map and the sensitivity of the crowd through a regional normalized cross-correlation loss and counting loss, to ensure the counting accuracy. Extensive experiments on five mainstream datasets demonstrate that Lw-Count achieves an optimal trade-off between counting performance and running speed compared with other methods.

In the hyperspectral image (HSI) classification, there are numerous deep learning-based research routes that have emerged recently. Among them, two methodologies attract our attention. One is CNN-based classification and the other is transformer-based classification. The essence of these two methodologies is to interchange information locally or at a long distance for HSI pixels in the spatial or spectral-spatial domain. There are two principles underlying this essence—the information mixing mechanism and the information mixing domain. Although both CNN-based and transformer-based have made efforts in these two principles and obtained favorable classification results, there is still room for improvement in terms of accuracy and efficiency. To further enhance the accuracy and efficiency under the two principles, fast Fourier transform (FFT) is introduced to HSI classification and a fast Fourier filter is designed to mix information efficiently in the frequency domain by means of FFT. The parametric-free characteristic and fast computation of FFT can assist us in efficiently learning interactions among features in the frequency domain. Furthermore, a fast Fourier filter block is built upon the fast Fourier filter for repeatedly using as a basic block. In addition, we propose a spectral-spatial convolution tokenizer (SSCT) to extract shallow features and prepare spectral-spatial tokens for fast Fourier filter blocks. Finally, by employing SSCT and fast Fourier filter blocks, a novel deep neural network architecture—fast Fourier filter network (F ³ Net) is proposed for HSI classification. F ³ Net-P as a pyramidal variant of F ³ Net is also investigated. Experimental results on four datasets comprehensively evaluate our models and indicate that they are competitive with several current state-of-the-art methods, especially when the training samples are extremely limited. Specifically, F ³ Net-P achieves the highest accuracy of 97.25%, 98.08%, 97.49% and 97.95% on the four datasets, respectively, outperforming second best compared model by 1.49%, 2.03%, 2.14% and 1.94%.

Deep learning (DL) has recently been a core ingredient in modern computer vision tasks, triggering a wave of revolutions in various fields. The hyperspectral image (HSI) classification task is no exception. A wide range of DL-based methods have shone brilliantly in HSI classification. However, understanding how to better exploit spectral and spatial information regarding HSI is still an open area of enquiry. In this article, we propose a hybrid convolution and hybrid resolution network with double attention for HSI classification. First, densely connected 3D convolutional layers are employed to extract preliminary spatial–spectral features. Second, these coarse features are fed to the hybrid resolution module, which mines the features at multiple scales to obtain high-level semantic information and low-level local information. Finally, we introduce a novel attention mechanism for further feature adjustment and refinement. Extensive experiments are conducted to evaluate our model in a holistic manner. Compared to several popular methods, our approach yields promising results for four datasets.