When humans delineate objects of interest from high-resolution remote sensing (RS)
images, they first determine the existence and locations of these objects. Then, they identify …

Due to the undulating topography, the world contains numerous irregularly shaped small
fields. Autonomous navigation along the curved edges of these fields is crucial for achieving …

Semantic segmentation of surface defects is essential to ensure product quality in intelligent
manufacturing. However, due to the diversity and complexity of industrial scenarios and …

Fabric defect detection has always been a key issue, and it positively correlated its efficiency
with productivity. From manual visual methods to machine vision and deep learning-based …

Real-time semantic segmentation of street scenes is an essential and challenging task for
autonomous driving systems, which needs to achieve both high accuracy and efficiency …

Railway scene understanding is key to autonomous train operation and important in active
train perception. However, most railway scene understanding methods focus on track …

Semantic segmentation is an essential technology for self-driving cars to comprehend their
surroundings. Currently, real-time semantic segmentation networks commonly employ either …

Data augmentation is a crucial component of machine learning. In 2-D object detection
tasks, it can significantly enhance the performance of detectors without increasing the …

Real-time semantic segmentation of remote-sensing images demands a trade-off between
speed and accuracy, which makes it challenging. Apart from manually designed networks …

Autonomous driving requires a structured understanding of the surrounding road maps and
networks to navigate. However, considering the flexibility of autonomous vehicles and the …