融合注意力分支特征的甲烷泄漏红外图像分割

甲烷是现代化工业生产和社会生活的重要能源之一，实现其有效探测与分割对于及时发现甲烷泄漏事故并识别其扩散范围具有重要意义。针对红外成像条件下甲烷气体图像的轮廓模糊、泄漏的甲烷气体与背景对比度较低、形状易受大气流动因素影响等问题，本文提出一种融合注意力分支特征的红外图像分割网络（Attention Branch Feature Network,ABFNet）实现甲烷气体泄漏探测。首先，为增强模型对红外甲烷气体图像的特征提取能力，设计分支特征融合模块将残差模块1和残差模块2的输出特征与残差模块3以逐像素相加的方法融合，获取红外甲烷气体图像丰富细致的特征表达以提高模型识别精度。其次，为进一步加快模型的推理速度，将标准瓶颈单元中的3×3卷积替换为深度可分离卷积，大幅度减少参数量达到实时检测甲烷气体泄漏。最后，将scSE注意力机制嵌入到分支特征融合模块，更多地关注扩散区域边缘和中心语义信息以克服红外甲烷气体轮廓模糊对比度低等问题提高模型的泛化能力。实验结果表明，本文提出的ABFNet模型AP50@95、AP50、AP60定量分割精度分别达到38.23%、89.63%和75.33%，相比于原始YOL...

Infrared Image Segmentation of Methane Leaks Incorporating Attentional Branching Features

Methane is an important energy source for modern industrial production and social life, and its effective detection and segmentation are important for the timely detection of methane leaks and identification of its diffusion range. To address image problems such as blurred contours of methane gas, low contrast between leaking methane gas and the background, and susceptibility of the shape to atmospheric flow factors under infrared imaging conditions, this study proposes an infrared image segmentation network (attention branch feature network (ABFNet)) incorporating attention branch features to achieve methane gas leak detection. First, to enhance the feature extraction capability of the model for IR methane gas images, a branch feature fusion module was designed to fuse the output features of residual modules 1 and 2 with residual module 3 in a pixel-by-pixel summing method to obtain rich and detailed feature expressions of IR methane gas images to improve the model's recognition accuracy. Second, to further accelerate the inference speed of the model, the 3×3 convolution in the standard bottleneck unit was replaced with a depth-separable convolution to significantly reduce the number of parameters required for real-time methane gas leak detection. Finally, scSE attention mechanisms were embedded in the branching feature fusion module to focus more on the edge and center semantic information of the diffusion region to overcome the problem of low contrast of the blurred IR methane gas contours and improve the generalization ability of the model. The experimental results showed that the quantitative segmentation accuracy of the proposed ABFNet models AP50@95, AP50, and AP60 reached 38.23%, 89.63%, and 75.33%, respectively, with improvements of 4.66%, 3.76%, and 7.04%, respectively, compared with the segmentation accuracy of the original YOLACT model. The inference speed reached 34.99 frames/s and met the demand of real-time detection. The experimental results verified the effectiveness and engineering practicality of the proposed algorithm for infrared methane leak detection.