Abstract Machine vision significantly improves the efficiency, quality, and reliability of defect
detection. In visual inspection, excellent optical illumination platforms and suitable image …

With the rapid development of deep learning techniques, deep convolutional neural
networks (DCNNs) have become more important for object detection. Compared with …

Typical diffusion models are trained to accept a particular form of conditioning, most
commonly text, and cannot be conditioned on other modalities without retraining. In this …

Object detection is a difficult downstream task in computer vision. For the on-board edge
computing platforms, a giant model is difficult to achieve the real-time detection requirement …

We propose a novel Attentional Scale Sequence Fusion based You Only Look Once (YOLO)
framework (ASF-YOLO) which combines spatial and scale features for accurate and fast cell …

One-to-one set matching is a key design for DETR to establish its end-to-end capability, so
that object detection does not require a hand-crafted NMS (non-maximum suppression) to …

Object detection is a well-known task in the field of computer vision, especially the small
target detection problem that has aroused great academic attention. In order to improve the …

Object detection on drone-captured scenarios is a recent popular task. As drones always
navigate in different altitudes, the object scale varies violently, which burdens the …

Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging
task. The traditional image processing method relies heavily on manmade features, which is …

In the research of computer vision, a very challenging problem is the detection of small
objects. The existing detection algorithms often focus on detecting full-scale objects, without …