With the progressive advancement of remote sensing image technology, its application in the agricultural domain is becoming increasingly prevalent. Both cultivation and transportation processes can greatly benefit from utilizing remote sensing images to ensure adequate food supply. However, such images often exist in harsh environments with many gaps and dense distribution, which poses major challenges to traditional target detection methods. The frequent missed detections and inaccurate bounding boxes severely constrain the further analysis and application of remote sensing images within the agricultural sector. This study presents an enhanced version of the YOLO algorithm, specifically tai... More >

Maneuvering target tracking is widely used in unmanned vehicles, missile navigation, underwater ships, etc. Due to the uncertainty of the moving characteristics of maneuvering targets and the low sensor measurement accuracy, trajectory tracking has always been an open research problem and challenging work. This paper proposes a trajectory estimation method based on LSTM neural network for uncertain motion characteristics. The network consists of two LSTM networks with stacked serial relationships, one of which is used to predict the movement dynamics, and the other is used to update the track's state. Compared with the classical Kalman filter based on the maneuver model, the method proposed... More >

Nowadays, state estimation is widely used in fields such as autonomous driving and drone navigation. However, in practical applications, it is difficult to obtain accurate target motion models and noise covariance.This leads to a decrease in the estimation accuracy of traditional Kalman filters. To address this issue, this paper proposes an adaptive model free state estimation method based on attention parameter learning module. This method combines Transformer's encoder with Long Short Term Memory Network (LSTM), and obtains the system's operational characteristics through offline learning of measurement data without modeling the system dynamics and measurement characteristics. In addition,... More >

In recent years, deep learning techniques have been increasingly applied to the detection of remote sensing images. However, the substantial size variation and dense distribution of objects in these images present significant challenges to detection algorithms. Current methods often suffer from low efficiency, missed detections, and inaccurate bounding boxes. To address these issues, this paper presents an improved YOLO algorithm, YOLOv7-bw, designed for efficient remote sensing image detection, thereby advancing object detection applications in the remote sensing industry. YOLOv7-bw enhances the original SPPCSPC pooling pyramid network by incorporating a Bi-level Routing Attention module, w... More >

To predict future trends based on the data from sensors is an important technology for many applications, such as the Internet of Things, smart cities, etc. Based on the predicted results, further decisions and system controls can be made. Raw sensor data sets are often complex non-linear data with noise, which results in the difficulty of accurate prediction. This paper proposes a distributed deep prediction network based on a covariance intersection (CI) fusion algorithm in which the deep learning networks, such as long-term and short-term memory networks (LSTM) and gated recurrent unit networks (GRU) are fused by CI fusion algorithm to effectively develop the performance of prediction. Mo... More >