Wang Wenyu, Zhao Yanqin, Xue Zhi
School of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin, Heilongjiang, China.
College of Science, Heilongjiang University of Science and Technology, Harbin, Heilongjiang, China.
PeerJ Comput Sci. 2024 Sep 16;10:e2313. doi: 10.7717/peerj-cs.2313. eCollection 2024.
To address issues such as misdetection and omission due to low light, image defocus, and worker occlusion in coal-rock image recognition, a new method called YOLOv8-Coal, based on YOLOv8, is introduced to enhance recognition accuracy and processing speed. The Deformable Convolution Network version 3 enhances object feature extraction by adjusting sampling positions with offsets and aligning them closely with the object's shape. The Polarized Self-Attention module in the feature fusion network emphasizes crucial features and suppresses unnecessary information to minimize irrelevant factors. Additionally, the lightweight C2fGhost module combines the strengths of GhostNet and the C2f module, further decreasing model parameters and computational load. The empirical findings indicate that YOLOv8-Coal has achieved substantial enhancements in all metrics on the coal rock image dataset. More precisely, the values for AP, AP, and AR were improved to 77.7%, 62.8%, and 75.0% respectively. In addition, (oLRP) were decreased to 45.6%. In addition, the model parameters were decreased to 2.59M and the FLOPs were reduced to 6.9G. Finally, the size of the model weight file is a mere 5.2 MB. The enhanced algorithm's advantage is further demonstrated when compared to other commonly used algorithms.
为了解决煤岩图像识别中由于光线不足、图像散焦和工人遮挡等问题导致的误检和漏检问题，引入了一种基于YOLOv8的名为YOLOv8-Coal的新方法，以提高识别精度和处理速度。可变形卷积网络版本3通过用偏移量调整采样位置并使其与物体形状紧密对齐来增强物体特征提取。特征融合网络中的极化自注意力模块强调关键特征并抑制不必要的信息，以最小化无关因素。此外，轻量级C2fGhost模块结合了GhostNet和C2f模块的优势，进一步减少了模型参数和计算量。实证结果表明，YOLOv8-Coal在煤岩图像数据集的所有指标上都有显著提升。更确切地说，AP、AP和AR的值分别提高到了77.7%、62.8%和75.0%。此外，(oLRP)降低到了45.6%。此外，模型参数减少到2.59M，FLOPs减少到6.9G。最后，模型权重文件的大小仅为5.2MB。与其他常用算法相比，该增强算法的优势进一步得到了证明。
