4.7 Article

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

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2022.3153997

Keywords

Atrous spatial pyramid pooling (ASPP); defect detection; feature fusion; object detection; printed circuit board (PCB)

Funding

  1. National Natural Science Foundation of China [62073271]
  2. International Science and Technology Cooperation Project of Fujian Province of China [2019I0003]
  3. Independent Innovation Foundation of AECC [ZZCX-2018-017]

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In this article, a novel enhanced multiscale feature fusion method called ABFPN is proposed to improve the detection performance of small objects. It is evaluated on benchmark datasets and applied to detect surface defects on printed circuit boards. Experimental results demonstrate its reliability and efficiency.
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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