Recently, visual simultaneous localization and mapping (SLAM) has been widely used in robotics and autonomous vehicles. It performs well in static environments. However, real-world environments are often dynamic scenarios. Because it is difficult for SLAM to deal with moving objects such as pedestrians and moving cars, SLAM does not meet the actual needs of robots and autonomous vehicles in real-world scenarios. Visual odometry (VO) is a key component of SLAM systems. In this paper, to extend SLAM to dynamic scenarios, we propose a monocular VO based on direct sparse odometry (DSO) to solve the problems arising in a dynamic environment. The proposed method, called DSO-Dynamic (DSOD), combines a semantic segmentation network with a depth prediction network to provide prior depth and semantic information. Experiments were conducted on the KITTI and Cityscapes datasets, and the results show our method achieves good performance compared with the baseline algorithm, DSO.

Object tracking has become widespread in many fields, such as autonomous vehicles, video surveillance and robotics. However, it is far from the requirements for real-world applications. Recently, Siamese network based trackers have attracted high attention by balancing accuracy and speed. Because these trackers only learn a similarity measurement model via off-line training, the exemplar branch has insufficient discriminant information to adapt to the constantly changing appearance of the target in subsequent frames. We propose a Siamese network based tracker that improves upon tracking performance as follows. First, an adaptive background superposition initialization is proposed and used in the exemplar branch to make full use of the limited prior information in the first frame. Second, a light-weight convolutional neural network is proposed and applied as the tracker's backbone; it compresses the dimensions of the feature to ensure speed and accuracy. Third, the channel attention module is introduced into our tracker and integrated with adaptive background superposition initialization. The feature map of the original exemplar image and its background changed image are adjusted by a channel attention model and fused to enhance the representation of the exemplar image. The GOT-10k dataset is applied to train our tracker. Finally, experiments on the object tracking benchmark (OTB) and visual object tracking (VOT) demonstrate the effectiveness of our proposed approach compared with state-of-the-art trackers.

Plant disease control has long been a critical issue in agricultural production and relies heavily on the identification of plant diseases, but traditional disease identification requires extensive experience. Most of the existing deep learning-based plant disease classification methods run on high-performance devices to meet the requirements for classification accuracy. However, agricultural applications have strict cost control and cannot be widely promoted. This paper presents a novel method for plant disease classification using a binary neural network with dual attention (DABNN), which can save computational resources and accelerate by using binary neural networks, and introduces a dual-attention mechanism to improve the accuracy of classification. To evaluate the effectiveness of our proposed approach, we conduct experiments on the PlantVillage dataset, which includes a range of diseases. The $F1score$ and $Accuracy$ of our method reach 99.39% and 99.4%, respectively. Meanwhile, compared to AlexNet and VGG16, the $Computationalcomplexity$ of our method is reduced by 72.3% and 98.7%, respectively. The $Paramssize$ of our algorithm is 5.4% of AlexNet and 2.3% of VGG16. The experimental results show that DABNN can identify various diseases effectively and accurately.