💡💡💡本文独家改进:改进点:1)backbone加入CBAM;2)backbone、neck连接处加入involution注意力;3)添加一个针对小物体的额外预测头,提升小目标检测性能; HIC-YOLOv8| 亲测在多个数据集能够实现大幅涨点,尤其在VisDrone-2019涨点显著, VisDrone-2019-DET 数据集上将 mAP95 提高了 6.42%,将 mAP@0.5 提高了 9.38...
论文题目:Small object detection in UAV image based on improved YOLOv5(基于改进YOLOv5的无人机小目标检测) 作者:Jian Zhang, Guoyang Wan, Ming Jiang, Guifu Lu, Xiuwen Tao & Zhiyuan Huang 期刊:S…
为了解决遥感小目标检测任务中特征表示不足、背景混淆等问题,本文基于YOLOV5框架进行改进,通过增加三个即插即用的模块:特征增强模块(FEM)、特征融合模块(FFM)和空间上下文感知模块(SCAM),显著增强了网络的局部感知能力、多尺度特征融合能力和跨通道、跨空间的全局关联能力,同时尽量避免增加复杂度。利用VEDAI和AI-TOD两...
YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles PDF: https://arxiv.org/pdf/2112.11798.pdf PyTorch代码: https://github.com/shanglianlm0525/CvPytorch PyTorch代码: https://github.com/shanglianlm0525/PyTorch-Networks ...
Considering that traditional object detection algorithms have low accuracy in handling PCB images with complex backgrounds, various types, and small-sized defects, this paper proposes a PCB defect detection algorithm based on a novel YOLOv5 multi-scale attention mechanism(EMA) spatial pyramid dilated ...
Wu X, Hong D, Chanussot J (2022) Uiu-net: U-net in u-net for infrared small object detection. IEEE transactions on image processing 32:364–376 Google Scholar Mahaur B, Mishra K (2023) Small-object detection based on yolov5 in autonomous driving systems. Pattern Recogn Lett 168:115–...
FFCA-YOLO(Feature Enhancement, Fusion, and Context Aware YOLO)是一种专为遥感图像中的小目标检测设计的模型,它在YOLOv5的基础上进行了显著的改进,通过引入特征增强模块(FEM)、特征融合模块(FFM)和空间上下文感知模块(SCAM)来提高模型在检测小目标时的准确性和效率。以下是对FFCA-YOLO模型特点、应用场景、如何适应...
Discussed in #10083 Originally posted by CesareDavidePace November 8, 2022 Hi, I would need help with regards to the detection of very small objects, we are talking about objects even 20x20 on images of 5000x3000. My approach was to use ...
However, the computation cost of these models is large, which makes deploying a real-time object detection system unfeasible, while leaving room for improvement. To this end, an improved YOLOv5 model: HIC-YOLOv5 is proposed to address the aforementioned problems. Firstly, an additional prediction...
Common backbones in object detection algorithms are used to detect multi-scale objects. The receptive field of the backbone module is extremely large. In YOLOv4, the input of the backbone called CSPDarknet53 [7] is 725×725725×725. However, the number of pixels of the small object in ...