DWHA-PCMSP: Salient Object Detection Network in Coal Mine Industrial IoT

With the development of intelligent technology in coal mine industrial Internet of Things (IoT), the demand for salient object detection (SOD) in underground coal mine space has been increasing. The complex scenes and variable backgrounds in coal mine bring challenges for SOD, such as blurred edges, high computational complexity, and long processing times, making it difficult to meet the accuracy and real-time requirements of coal mine industrial IoT applications. To address these issues, we propose the dynamic weighting hybrid attention (DWHA)-partial convolution multiscale strip pooling (PCMSP) network for SOD in the coal mine industrial IoT. First, we introduce the DWHA module, which dynamically fuses self-attention for global context and SBAM for refining channel and spatial information, improving saliency detection accuracy. Second, we propose the PCMSP lightweight module, which the multiscale strip pooling introduces multiscale dilations, enhancing the ability to capture multiscale information and improve feature representation. By using partial convolution, which reduces the consumption of computing resources and running time while ensuring boundary quality. The experimental results indicate that, using self-built dataset for underground coal mine SOD, the DWHA-PCMSP network outperforms the four SOTA: BASNet, U2Net, SUCA, and EDN by achieving an increase of 2.82% in F1-score, a decrease of 23.70% in MAE, a reduction of 72.3G in FLOPs, and an improvement of 6.6 FPS in speed, compared to the worst-performing model.