Zhou Jieyang, Ning Jing, Xiang Zhiyang, Yin Pengfei
College of Computer Science and Engineering, Jishou University, Jishou 416000, China.
School of Communication and Electronic Engineering, Jishou University, Jishou 416000, China.
Sensors (Basel). 2024 Jul 3;24(13):4333. doi: 10.3390/s24134333.
A robust wood material crack detection algorithm, sensitive to small targets, is indispensable for production and building protection. However, the precise identification and localization of cracks in wooden materials present challenges owing to significant scale variations among cracks and the irregular quality of existing data. In response, we propose a crack detection algorithm tailored to wooden materials, leveraging advancements in the YOLOv8 model, named ICDW-YOLO (improved crack detection for wooden material-YOLO). The ICDW-YOLO model introduces novel designs for the neck network and layer structure, along with an anchor algorithm, which features a dual-layer attention mechanism and dynamic gradient gain characteristics to optimize and enhance the original model. Initially, a new layer structure was crafted using GSConv and GS bottleneck, improving the model's recognition accuracy by maximizing the preservation of hidden channel connections. Subsequently, enhancements to the network are achieved through the gather-distribute mechanism, aimed at augmenting the fusion capability of multi-scale features and introducing a higher-resolution input layer to enhance small target recognition. Empirical results obtained from a customized wooden material crack detection dataset demonstrate the efficacy of the proposed ICDW-YOLO algorithm in effectively detecting targets. Without significant augmentation in model complexity, the mAP50-95 metric attains 79.018%, marking a 1.869% improvement over YOLOv8. Further validation of our algorithm's effectiveness is conducted through experiments on fire and smoke detection datasets, aerial remote sensing image datasets, and the coco128 dataset. The results showcase that ICDW-YOLO achieves a mAP50 of 69.226% and a mAP50-95 of 44.210%, indicating robust generalization and competitiveness vis-à-vis state-of-the-art detectors.
一种对小目标敏感的强大木材材料裂缝检测算法对于生产和建筑保护至关重要。然而，由于裂缝之间存在显著的尺度变化以及现有数据质量不规则，木材材料中裂缝的精确识别和定位面临挑战。为此，我们提出了一种针对木材材料定制的裂缝检测算法，利用YOLOv8模型的进展，命名为ICDW-YOLO（改进的木材材料裂缝检测-YOLO）。ICDW-YOLO模型为颈部网络和层结构引入了新颖设计，以及一种锚定算法，其具有双层注意力机制和动态梯度增益特性，以优化和增强原始模型。最初，使用GSConv和GS瓶颈构建了一种新的层结构，通过最大限度地保留隐藏通道连接来提高模型的识别准确率。随后，通过聚集-分布机制对网络进行增强，旨在增强多尺度特征的融合能力，并引入更高分辨率的输入层以增强小目标识别。从定制的木材材料裂缝检测数据集中获得的实证结果证明了所提出的ICDW-YOLO算法在有效检测目标方面的有效性。在模型复杂度没有显著增加的情况下，mAP50-95指标达到79.018%，比YOLOv8提高了1.869%。通过在火灾和烟雾检测数据集、航空遥感图像数据集以及coco128数据集上的实验进一步验证了我们算法的有效性。结果表明，ICDW-YOLO实现了69.226%的mAP50和44.210%的mAP50-95，相对于最先进的检测器显示出强大的泛化能力和竞争力。
