###### Abstract  
Straw burning is a long-term environmental problem in China's agricultural production. At present, China relies mainly on satellite remote sensing positioning and manual patrol to detect straw burning, which are inefficient. Due to the development of machine learning, target detection technology can be used for the detection of straw burning, but the current research does not take into account the various scenarios of straw burning and the deployment of object detection models. Therefore, a lightweight network based on depthwise separable convolution and channel attention mechanisms is proposed to detect straw-burning smoke at a remote distance. Various regional and crop-burning smoke datasets were collected to make the algorithm more robust. The lightweight network was applied to automatically identify and detect straw-burning smoke in surveillance videos. The experiment showed that the amount of light network parameter was only 4.76 M, and the calculation performance was only 11.2 Gflops. For the intelligent detection of straw-burning smoke, performance verification accuracy was improved by 2.4% compared with Yolov5s. Meanwhile, the detection speed on the embedded Jetson Xavier NX device can reach 28.65 FPS, which is 24.67% better than the Yolov5s. This study proposes a lightweight target detection network, providing a possible method for developing low-cost, rapid straw-burning smoke detection equipment. s 2023 202  
generated by straw burning can be monitored at long distances, it is feasible to judge straw burning by monitoring smoke. Thus, an urgent need to develop a high-precision, non-contact, rapid, remote detection method for straw-burning smoke exists. Using target detection technology to build a straw-burning smoke target detection system can help to meet the above requirements and is highly suitable for use by law enforcement agencies to detect straw-burning smoke. The development of deep learning opens a new route to straw-burning smoke detection. Researchers have successfully applied deep learning for smoke detection and have obtained higher than traditional detection accuracy and speed using the target detection technology to build the straw-burning smoke target detection system. This system can meet the above requirements and is suitable for enforcing straw-burning smoke. Currently, the commonly used target detection algorithms include Faster R-CNN (Region-Convolutional Neural Network) [7], SSD (Single Shot MultiBox Detector) [8], and Yolo (You Only Live Once) [9; 10; 11]. In terms of straw-burning smoke detection, [PERSON] et al. [12] proposed a smoke-detection method based on an improved frame difference method and Faster R-CNN. For smoke detection, it first uses the improved frame difference method to extract candidate regions, then it uses the Faster R-CNN model for smoke detection. The model in this paper uses Faster R-CNN with slow reasoning speed and the inter-frame difference method to filter data, which cannot meet the needs of real-time monitoring by law enforcement personnel. Anhui Baolong Environmental Protection Technology [13] have also attempted to add the straw-burning smoke detection function to their remote sensing monitoring equipment; however, within the actual applications, the smoke-detection speed of this equipment is low. [PERSON] et al. [14] proposed using the improved Yolov5s algorithm to detect smoke in Sentinel-2 images captured through remote sensing. A convolutional block attention module was added to the original model to improve detection accuracy. However, this algorithm is used for remote sensing monitoring. In practice, monitoring the small area of straw burning is impossible. [PERSON] et al. [15] and others conducted studies by placing a small arithmetic platform on an unmanned aerial vehicle, and used Yolov5 for real-time reasoning to complete outdoor wildfire remote monitoring.