The aim of camouflaged object detection (COD) is to discern concealed objects within the background. Due to issues such as high similarity to the surrounding environment, small size, occlusions, COD is considered a highly challenging task. In this paper, we propose a novel COD framework, named multi-clue sliding window attention network (MCSWA-Net), stressing in utilizing prior knowledge at different semantic levels to guide the detection of camouflaged objects via multi-scale sliding window attention (MSWA). To this end, we first devise the dynamic local detail capture (DLC) module and the global interactive decoder (GID) module to generate both local and global guidance clues. Particularly, each block of the DLC module produces local prior clue by processing corresponding image features at each stage from the encoder. And the GID module fuses all adjacent encoder features, generates global prior clue by combining fusion features of multi-semantic levels. Further, to make full use of prior clues guiding the detection of camouflaged objects at multi-semantic levels, we design the multi-scale guidance attention fusion (MAF) module and use two prior clues to refine the image features via the group fusion and the MSWA separately. Experiments conducted on four COD benchmark datasets, and results demonstrate that our MCSWA-Net is superior to state-of-the-art (SOTA) COD methods. In addition, we explore the detection capabilities of our MCSWA-Net for the downstream vision tasks related to COD, such as polyp segmentation, COVID-19 lung infection segmentation, and industrial defect detection. Experimental results show the proposed method has high degree of generality.

The application scenarios of automated robots are undergoing a paradigm shift from structured environments to unstructured, complex settings. In highly constrained settings like factory inspections or disaster rescue, conventional steering systems show clear drawbacks. While the four-wheel independent drive and independent steering (4WS) robot provides a variety of steering modes, which can effectively meet the needs of complex environments. However, how a 4WS robot autonomously selects different steering modes based on trajectory point information during trajectory tracking remains a challenging problem. This paper proposes a multi-modal trajectory tracking method considering the switch of steering modes, which decomposes the trajectory tracking task into two parts: mode decision-making and tracking control. The corresponding method is designed based on deep reinforcement learning. Additionally, a target trajectory random generator and corresponding training interaction environment are designed to train the model in a data-driven manner. In the designed scenario, our tracker achieve more than a 30% improvement in average tracking error across all motion modes compared with model predictive control, and the decider’s average decision position error is less than 2 cm. Extensive experiments demonstrate that our method achieves superior tracking performance and real-time capabilities compared to current methods.

Regular temperature measurement of critical parts of an annealing furnace has always been a difficult task. Due to the harsh environment of high temperature, high noise, and darkness in the annealing furnace operation area, unmanned vehicles equipped with the RGB-T semantic segmentation model are usually adopted in most factories for inspection. However, existing RGB-T semantic segmentation models usually rely on good lighting or thermal conditions, which are generally difficult to fulfill in annealing furnace operation areas. In this paper, we propose a new hierarchical fusion-based semantic enhancement network, HFSENet. We first adopt the two-stream structure and the siamese structure to extract the low-level and high-level features of unimodal modalities, respectively. Then, considering the differences between the features in different hierarchical levels, we introduce a novel low-level feature spatial fusion module and a high-level feature channel fusion module to perform the multi-modal feature hierarchical fusion. On this basis, we also propose the semantic feature complementary enhancement module, which utilizes the appearance information set and object information set extracted from RGB and thermal infrared (TIR) branches to enhance the fused features and give them more semantic information. Finally, segmentation results with refined edges are obtained by an edge refinement decoder that includes a local search extraction module. The unmanned inspection vehicle we built with the proposed HFSENet has successfully passed the test, and the recognition performance of the four targets exceeds the current state-of-the-art (SOTA) method on our homemade annealing furnace operation area dataset.

In the context of unstructured and unknown environment, the autonomous navigation still faces many challenges, such as assessing rough terrain and deciding how to safely navigate complex terrain. In this work, we propose a robust and practical off-road navigation framework that has been successfully deployed on a vibroseis truck for land exploration. First, in degraded wild scenes, a tightly coupled lidar-GNSS-inertial fusion odometry and mapping framework is adopted to construct a local point cloud map around the vehicle in real-time and provide precise localization. Then, based on amplitude-frequency characteristic analysis and point cloud PCA, a multi-layer terrain assessment map containing terrain roughness, obstacles and slope information is obtained. Finally, combining Gaussian distribution based adaptive sampler and Bayesian sequentially updated proposal distribution, a local graph is efficiently built to obtain multiple path solutions under constrained conditions. Both simulations and field experiments show that the proposed navigation framework can decide how to travel on a flat road even in harsh terrain conditions, naturally suppressing frequent attitude angle changes and preventing vehicle accidents.