A parallel compact Differential Evolution (pcDE) algorithm is proposed in this paper. The population is separated into multiple groups and the individual is run by using the method of compact Differential Evolution. The communication is implemented after predefined iterations. Two communication strategies are proposed in this paper. The first one is to replace the local optimal solution by global optimal solution in all groups, which is called optimal elite strategy (oe); the second one is to replace the local optimal solution by mean value of the local optimal solution in all groups, which is called mean elite strategy (me). Considering that the pcDE algorithm does not need to store a large number of solutions, the algorithm can adapt to the environment with weak computing power. In order to prove the feasibility of pcDE, several groups of comparative experiments are carried out. Simulation results based on the 25 test functions demonstrate the efficacy of the proposed two communication strategies for the pcDE. Finally, the proposed pcDE is applied to image segmentation and experimental results also demonstrate the superior quality of the pcDE compared with some existing methods.

The purpose of image dehazing is to remove the interference from weather factors in degraded images and enhance the clarity and color saturation of images to maximize the restoration of useful features. Single image dehazing is one of the most important tasks in the field of image restoration. In recent years, due to the progress of deep learning, single image dehazing has made great progress. With the success of Transformer in advanced computer vision tasks, some research studies also began to apply Transformer to image dehazing tasks and obtained surprising results. However, both the deconvolution-neural-network-based dehazing algorithm and Transformer based dehazing algorithm magnify their advantages and disadvantages separately. Therefore, this paper proposes a novel Transformer–Convolution fusion dehazing network (TCFDN), which uses Transformer’s global modeling ability and convolutional neural network’s local modeling ability to improve the dehazing ability. In the Transformer–Convolution fusion dehazing network, the classic self-encoder structure is used. This paper proposes a Transformer–Convolution hybrid layer, which uses an adaptive fusion strategy to make full use of the Swin-Transformer and convolutional neural network to extract and reconstruct image features. On the basis of previous research, this layer further improves the ability of the network to remove haze. A series of contrast experiments and ablation experiments not only proved that the Transformer–Convolution fusion dehazing network proposed in this paper exceeded the more advanced dehazing algorithm, but also provided solid and powerful evidence for the basic theory on which it depends.

This paper introduces Re-DQN, a deep reinforcement learning-based algorithm for comprehensive coverage path planning in lawn mowing robots. In the fields of smart homes and agricultural automation, lawn mowing robots are rapidly gaining popularity to reduce the demand for manual labor. The algorithm introduces a new exploration mechanism, combined with an intrinsic reward function based on state novelty and a dynamic input structure, effectively enhancing the robot’s adaptability and path optimization capabilities in dynamic environments. In particular, Re-DQN improves the stability of the training process through a dynamic incentive layer and achieves more comprehensive area coverage and shorter planning times in high-dimensional continuous state spaces. Simulation results show that Re-DQN outperforms the other algorithms in terms of performance, convergence speed, and stability, making it a robust solution for comprehensive coverage path planning. Future work will focus on testing and optimizing Re-DQN in more complex environments and exploring its application in multi-robot systems to enhance collaboration and communication.

This paper studies the lightweight deep learning object detection algorithm to detect ship targets in SAR images that can be deployed on-orbit and accessed in the space-based IoT. Traditionally, remote sensing data must be transferred to the ground for processing. With the vigorous development of the commercial aerospace industry, computing, and high-speed laser inter-satellite link technologies, the interconnection of everything in the intelligent world has become an irreversible trend. Satellite remote sensing has entered the era of a big data link with IoT. On-orbit interpretation gives remote sensing images expanse application space. However, implementing on-orbit high-performance computing (HPC) is difficult; it is limited by the power and computer resource consumption of the satellite platform. Facing this challenge, building a processing algorithm with less computational complexity, less parameter quantity, high precision, and low computational power consumption is a key issue. In this paper, we propose a lightweight end-to-end SAR ship detector fused with the vision transformer encoder: YOLO−ViTSS. The experiment shows that YOLO−ViTSS has lightweight features, the model size is only 1.31 MB; it has anti-noise capability is suitable for processing SAR remote sensing images with native noise, and it also has high performance and low training energy consumption with 96.6 mAP on the SSDD dataset. These characteristics make YOLO−ViTSS suitable for porting to satellites for on-orbit processing and online learning. Furthermore, the ideas proposed in this paper help to build a cleaner and a more efficient new paradigm for remote sensing image interpretation. Migrating HPC tasks performed on the ground to on-orbit satellites and using solar energy to complete computing tasks is a more environmentally friendly option. This environmental advantage will gradually increase with the current construction of large-scale satellite constellations. The scheme proposed in this paper helps to build a novel real-time, eco-friendly, and sustainable SAR image interpretation mode.