Accurate trajectory tracking is unrealistic in real-world scenarios, however, which is
commonly assumed to facilitate motion planning algorithm design. In this paper, a safe and …

Optimizing the computational efficiency of the artificial neural networks is crucial for resource-
constrained platforms like autonomous driving systems. To address this challenge, we …

This paper proposes a lightweight and efficient railway region extraction model LRseg,
which provides technical support for detecting foreign objects on the railway. LRseg consists …

Deep learning plays an important role in crack segmentation, but most work utilize off-the-
shelf or improved models that have not been specifically developed for this task. High …

Accurate segmentation of multiple targets, such as ribs, clavicles, heart, and lung fields, from
chest X-ray images is crucial for diagnosing various lung diseases. Currently, mainstream …

Autonomous driving has witnessed rapid development with the application of artificial
intelligence technology in recent years. Lane detection is one of the tasks of environment …

Medical image analysis, particularly the segmentation of White Blood Cells (WBCs), holds
critical importance in scrutinizing the quantity and morphology of these cells in smear …

This paper presents FUSegNet, a new model for foot ulcer segmentation in diabetes
patients, which uses the pre-trained EfficientNet-b7 as a backbone to address the issue of …

Recent real-time semantic segmentation methods usually adopt an additional semantic
branch to pursue rich long-range context. However, the additional branch incurs undesirable …

Autonomous vehicles equipped with numerous advanced sensors are capable of obtaining
road preview information, creating new opportunities for vehicle suspension systems. This …