Object detection is one of the essential tasks in a variety of real-world applications such as
autonomous driving and robotics. In a real-world scenario, unfortunately, there are …

An innovative and improved method for infrared object detection, namely DBD-YOLOv8
(DCN-BiRA-DyHeads-YOLOv8), is presented. The inherent limitations of the YOLOv8 model …

Existing lightweight salient object detection (SOD) methods aim to solve the problem of high
computational costs that is prevalent with heavyweight methods. However, compared with …

Salient object detection methods based on two-modal images have achieved remarkable
success with the aid of image acquisition equipment. However, environmental factors often …

Existing works mainly focus on how to aggregate multi-level features for salient object
detection, which may generate sub-optimal results due to interference with redundant …

Weakly supervised salient object detection (SOD) is a challenging task and has drawn much
attention from several research perspectives, it has revealed two problems while driving the …

Salient object detection (SOD) is a critical task in computer vision, aimed at identifying
visually striking regions within images. Existing SOD methods predict saliency maps in a …

Salient object detection (SOD) is a challenging and fundamental research in computer
vision and image processing. Since the cost of pixel-level annotations is high, scribble …

To address the challenges of object detection in complex remote sensing imagery, where
the YOLO backbone network struggles with adaptive learning of feature distributions …

Salient object detection (SOD) is a fundamental research topic in computer vision and has
attracted significant interest from various fields, it has revealed two issues while driving the …