Small object detection by Edge-aware Neural Network

被引:1
作者
Zhang, Xianhong [1 ]
Lu, Tao [1 ]
Wang, Jiaming [1 ]
Fu, Shichang [1 ]
Gao, Fangqun [1 ]
机构
[1] Wuhan Inst Technol, Hubei Key Lab Intelligent Robot, Wuhan 430073, Hubei, Peoples R China
基金
中国国家自然科学基金;
关键词
Small object detection; Equipment inspection; Multiple aggregations; Edge feature;
D O I
10.1016/j.engappai.2024.109406
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The object detection method is widely applied in industrial inspections. However, many detectors face challenges in accurately capturing the blurred edge details of small objects, resulting in inaccurate bounding box predictions. To address this, we propose an Edge-aware Neural Network (EANN) for small object detection. Firstly, we introduce a Channel and Spatial Attention Fusion Module (CSAFM) to enhance the edge features of small objects, enabling the network to extract more discriminative information. Next, we propose a Multiple Aggregation Feature Pyramid (MAFP) to integrate multi-scale deep features into shallow features. This fusion enriches the shallow features with abundant semantic information, thereby aiding in the detection of small objects. Additionally, we propose a Side and Center Point Aligned Intersection over Union loss (SCPAIoULoss) to enhance the bounding box regression when there is minimal overlap between predicted and ground truth boxes. SCPAIoULoss combines Side Ratio (SR) loss, Center Point Distance (CPD) loss, and Intersection over Union (IoU) loss. The utilization of SR Loss directly constrains the width and height regression of bounding boxes, while CPD loss introduces stricter constraints to facilitate bounding box regression. Furthermore, IoU loss promotes the overall regression of predicted boxes. We extensively experiment on Tiny CityPersons, WiderFace, and our proposed dataset of base station data centers to validate the effectiveness of our method. The results indicate that our method surpasses several State-of-The-Art (SOTA) approaches in small object detection and can be effectively applied to the task of inspecting base station data centers.
引用
收藏
页数:12
相关论文
共 77 条
[1]   SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network [J].
Bai, Yancheng ;
Zhang, Yongqiang ;
Ding, Mingli ;
Ghanem, Bernard .
COMPUTER VISION - ECCV 2018, PT XIII, 2018, 11217 :210-226
[2]   Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks [J].
Bell, Sean ;
Zitnick, C. Lawrence ;
Bala, Kavita ;
Girshick, Ross .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :2874-2883
[3]   Cascade R-CNN: Delving into High Quality Object Detection [J].
Cai, Zhaowei ;
Vasconcelos, Nuno .
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, :6154-6162
[4]   RRNet: A Hybrid Detector for Object Detection in Drone-captured Images [J].
Chen, Changrui ;
Zhang, Yu ;
Lv, Qingxuan ;
Wei, Shuo ;
Wang, Xiaorui ;
Sun, Xin ;
Dong, Junyu .
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW), 2019, :100-108
[5]   Beyond triplet loss: a deep quadruplet network for person re-identification [J].
Chen, Weihua ;
Chen, Xiaotang ;
Zhang, Jianguo ;
Huang, Kaiqi .
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, :1320-1329
[6]  
Chen Y, 2020, arXiv preprint arXiv:2004.12432, V2, P12
[7]   Surface Defect Detection Methods for Industrial Products: A Review [J].
Chen, Yajun ;
Ding, Yuanyuan ;
Zhao, Fan ;
Zhang, Erhu ;
Wu, Zhangnan ;
Shao, Linhao .
APPLIED SCIENCES-BASEL, 2021, 11 (16)
[8]   Towards Large-Scale Small Object Detection: Survey and Benchmarks [J].
Cheng, Gong ;
Yuan, Xiang ;
Yao, Xiwen ;
Yan, Kebing ;
Zeng, Qinghua ;
Xie, Xingxing ;
Han, Junwei .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 45 (11) :13467-13488
[9]   The Cityscapes Dataset for Semantic Urban Scene Understanding [J].
Cordts, Marius ;
Omran, Mohamed ;
Ramos, Sebastian ;
Rehfeld, Timo ;
Enzweiler, Markus ;
Benenson, Rodrigo ;
Franke, Uwe ;
Roth, Stefan ;
Schiele, Bernt .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :3213-3223
[10]   Attentional Local Contrast Networks for Infrared Small Target Detection [J].
Dai, Yimian ;
Wu, Yiquan ;
Zhou, Fei ;
Barnard, Kobus .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2021, 59 (11) :9813-9824