Object detection could be a technology associated with computer vision and image process that deals with crime squad work instances of semantics objects of a specific category in digital pictures and videos. Ap
Before the prevalent of deep learning, color and shape-based features are also used to address traffic sign detection problems [10]. With the rapid advancement of convolutional neural networks (CNNs) in deep learning, some deep learning-based small object detection methods have sprung up. However...
原文地址:Deep learning-based small object detection: A survey 主要贡献: 1、系统概述基于深度学习的 SOD 算法。 2、对基于深度学习的 SOTA SOD 算法进行性能评估。 3、最后,根据 SOD 的分类方法和性能分析,讨论了未来研究的潜在方向,包括适用于 SOD 优化的合适度量标准,弱监督 SOD 方法,多任务联合优化以及开放...
Small object detection in images is a significant challenge in computer vision due to issues like low resolution, occlusion, and scale variation, often resulting in existing models missing important details or requiring complex, large-scale setups. This paper introduces SO-YOLOv8, an enhanced version...
Object detection has been widely applied in various fields with the rapid development of deep learning in recent years. However, detecting small objects is still a challenging task because of the limited information in features and the complex background. To further enhance the detection accuracy of...
In the manufacturing process of printed circuit boards (PCBs), surface defects have a significant negative impact on product quality. Considering that traditional object detection algorithms have low accuracy in handling PCB images with complex backgroun
deep learning from five aspects, including multi-scale feature learning, data augmentation, training strategy, context-based detection and GAN-based detection. Then, we thoroughly analyze the performance of some typical small object detection algorithms on popular datasets, such as MS-COCO, PASCAL-VOC...
The importance of object detection within computer vision, especially in the context of detecting small objects, has notably increased. This thorough surve
Li et al.29 Improved the neck of YOLOv8, realized more advanced feature fusion, adopted GhostblockV2 module to replace C2f structure, and used WiseIoU loss function, thus improving the performance of small object detection. These deep learning models show relatively good performance in a variety...
Currently, lightweight small object detection algorithms for unmanned aerial vehicles (UAVs) often employ group convolutions, resulting in high Memory Access Cost (MAC) and rendering them unsuitable for edge devices that rely on parallel computing. To address this issue, we propose the SOD-YOLO mod...