A Small-Sized Object Detection Oriented Multi-Scale Feature Fusion Approach With Application to Defect Detection

计算机科学 棱锥(几何) 目标检测 人工智能 特征(语言学) 特征提取 水准点(测量) 模式识别(心理学) 联营 背景(考古学) 计算机视觉 数学 哲学 地理 古生物学 几何学 生物 语言学 大地测量学
作者
Nianyin Zeng,Peishu Wu,Zidong Wang,Han Li,Weibo Liu,Xiaohui Liu
出处
期刊:IEEE Transactions on Instrumentation and Measurement [Institute of Electrical and Electronics Engineers]
卷期号:71: 1-14 被引量:321
标识
DOI:10.1109/tim.2022.3153997
摘要

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 detection performance of small objects, in this article, a novel enhanced multiscale feature fusion method is proposed, namely, the atrous spatial pyramid pooling-balanced-feature pyramid network (ABFPN). In particular, the atrous convolution operators with different dilation rates are employed to make full use of context information, where the skip connection is applied to achieve sufficient feature fusions. In addition, there is a balanced module to integrate and enhance features at different levels. The performance of the proposed ABFPN is evaluated on three public benchmark datasets, and experimental results demonstrate that it is a reliable and efficient feature fusion method. Furthermore, in order to validate the applicational potential in small objects, the developed ABFPN is utilized to detect surface tiny defects of the printed circuit board (PCB), which acts as the neck part of an improved PCB defect detection (IPDD) framework. While designing the IPDD, several powerful strategies are also employed to further improve the overall performance, which is evaluated via extensive ablation studies. Experiments on a public PCB defect detection database have demonstrated the superiority of the designed IPDD framework against the other seven state-of-the-art methods, which further validates the practicality of the proposed ABFPN.
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