机电工程技术2024，Vol.53Issue(4)：125-128,213,5.DOI:10.3969/j.issn.1009-9492.2024.04.028
一种改进的基于深度学习的小目标检测方法
An Improved Deep Learning-based Approach for Small Object Detection
魏希来 1孙海江 1刘培勋 1孙兴龙1
作者信息
- 1. 中国科学院长春光学精密机械与物理研究所,长春 130033
- 折叠
摘要
Abstract
An improved deep learning-based method is presented for small object detection to address the limitations of current mainstream algorithms.These algorithms often require input images to be small-sized photos and suffer from excessive model parameters.To overcome these challenges,a preprocessing technique is proposed where a larger-sized image is divided into multiple smaller-sized images following specific rules.These fragmented images are then inputted into the network,eliminating the previous requirement of small-sized images for detection.The DNANet network structure is also enhanced by reducing its network layers,resulting in improved network inference speed.The loss function is optimized using TverskyLoss,a pixel segmentation loss function.Furthermore,a progressive learning approach is employed to train the model,ensuring a more stable transition from detecting ordinary to small objects.Experimental results demonstrate the effectiveness of the proposed method in enhancing the convergence speed of deep learning for small object detection in large-sized images.The improved network achieves a 5%increase in prediction accuracy for large-sized images and reduces prediction time by 25%.Consequently,the deep learning-based small object detection method presented in the study can be readily applied in engineering practice,offering significant practical value.关键词
小目标检测/深度学习/图像预处理/网络结构改进/渐进式学习Key words
small object detection/deep learning/image preprocessing/network architecture improvement/progressive learning分类
信息技术与安全科学引用本文复制引用
魏希来,孙海江,刘培勋,孙兴龙..一种改进的基于深度学习的小目标检测方法[J].机电工程技术,2024,53(4):125-128,213,5.
