DSSO-YOLO: A fast detection model for densely stacked small object

• Published in: Displays 2024-04, Vol.82, p.102659, Article 102659
• Main Authors: Zhang, Zheng, Liu, Liangchen, Zhao, Xunyi, Zhang, Lijun, Wu, Jun, Zhang, Yan, Li, Zhenghao
• Format: Article
• Language: English
• Subjects: Attention mechanisms; Bounding box regression; Densely stacked small object; YOLOv5s
• ISSN: 0141-9382; 1872-7387
• DOI: 10.1016/j.displa.2024.102659

Visual detection for densely stacked small object (DSSO) has a wide range of applications in the construction, logistics, and import/export industries. Take the construction industry as an example, intelligent rebar counting, can considerably improve the management efficiency in sales, delivery and inventory management. It can also effectively prevent acts such as supervisory theft. However, current mainstream object detection models are susceptible to complex backgrounds when applying directly to DSSO detection, and have not been optimized for highly similar objects with high regularity of arrangement, resulting in low detection accuracy and efficiency. Therefore, we propose the DSSO-YOLO model, which introduces the coordinate attention mechanism into the C3 module of YOLOv5s to increase the accuracy of detection. And the bounding box loss function CIoU in YOLOv5s is replaced with Focal-EIoU-Loss, in order to accelerate the convergence speed and further improve the regression accuracy. To verify the performance of the algorithm, we constructed the steel products dataset SPDC. According to the test results conducted on SPDC, compared with YOLOv5s, the F1-Score, recall, mAP@0.5 and mAP@0.5:0.95 increased by 2.0%, 2.6%, 2.1% and 1.5%, respectively. •A light-weight fast detection model for densely stacked small objects is proposed.•Accuracy is increased by combining attention mechanism and Focal-EIoU.•A steel products dataset with the name of SPDC is built.