PP-YOLO-CS: A Novel Approach for Real-Time Fire and Smoke Detection in Industrial Park Environments

Real-time detection of fire and smoke in Industrial park environments is crucial for ensuring safety. Current models face significant challenges in accurately detecting fire and smoke in scenarios with small-sized targets and substantial shape variations, and there is an urgent need to enhance the detection capabilities of the models. Addressing these challenges, this paper proposes the novel PP-YOLO-CS model, based on the PP-YOLOE, integrated CA-ConvNeXt, SP-ASPP, and SP-CSP modules. The model introduces CA-ConvNeXt, which integrates Channel Attention with ConvNeXt, focusing on critical information channels. This improves feature extraction efficiency and accuracy in complex industrial park fire scenarios and addresses the shortcomings of current models in handling complex and diverse data. We propose the Split-based Convolution with Atrous Spatial Pyramid Pooling Structure (SP-ASPP) that captures multi-scale information through varied dilation rates and channel-splitting techniques, effectively addressing target scale and shape variation challenges, enhancing detection accuracy across diverse sizes while maintaining computational efficiency. We present the Split-based Convolution with Cross Stage Partial (SP-CSP) framework, a distinctive approach that excels in precision. The framework integratesthe Atrous Spatial Pyramid Pooling (ASPP) module and Split-based Convolution, effectively enlarging the receptive field to capture finer details, especially in low-resolution images, while simplifying the feature fusion process, reducing information redundancy, and improving the accuracy of the system. To facilitate the development of a fire smoke detection model for industrial park environments, we establish the IndustrialParkData dataset, comprising 7141 images depicting fire and smoke in factory environments. Experimental results demonstrate that CA-ConvNeXt and SP-ASPP improves the accuracy of the PP-YOLOE model by 1.73%, with a simultaneous speed increase of 6.39FPS. Furthermore, CA-ConvNeXt and SP-CSP maintains the same prediction speed as PP-YOLOE while increasing the Average Precision (mAPs) by 2.05%. These results demonstrate the strong performance of the PP-YOLO-CS model in detecting fire and smoke.