## 3 Results
### Performance Analysis  
To further verify the network's detection performance for straw-burning smoke, we collected 1000 straw-burning smoke pictures in different backgrounds to test the performance of the networks. Different backgrounds include paddy fields, corn fields, and wheat fields in spring and autumn. At the same time, in order to verify the robustness of the algorithm, some unfamiliar backgrounds were added, including green lawns and low visibility fields. The performance test index is the straw-burning smoke target-recognition rate, and its calculation formula is shown in (10). \[Recognition\ rate=\frac{The\ number\ of\ correct\ picture}{Total\ number\ of\ pictures}\times 100 \tag{10}\]  
The number of correct pictures function shows the number of pictures correctly recognized by the model (no false and no missed detections). In Figure 9, the scene shows a paddy field in Luan in spring. The background has a cement floor similar to the color of the straw-burning smoke and the field is green. The color contrast of the picture is high, which makes it easy to encounter errors. From the test results, it can be seen that Yolov5s and Yolov5s+DConv missed the smoke in the diagram. Yolov5s+ECA encountered a mistake in identifying roads as smog; Yolo-Light did not show any mis-detection and correctly detected smoke. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Inference Time** & **FPS on Jetson** \\ \hline Yolov5s & 43.3 ms & 22.57 \\ Yolov5s+ECA & 44.7 ms & 22.37 \\ Yolov5s+Dconv & 34.2 ms & 29.24 \\ Yolo-Light & 34.9 ms & 28.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison of models on Jetson Xavier NX. In Figure 10, the scene shows a paddy field in Luan in autumn. There is no straw burning in this scene. The main picture is yellow and displays mature rice. There is a reflective pond in the paddy field. Under the same background, the algorithm is prone to misinterpret the reflections of the pond as smoke. From the test results, Yolov5s mistakenly identified the pond as smoke; [PERSON]+ECA, Yolov5s+DConv, and Yolo-Light were not mistaken. The scene in Figure 11 shows a green lawn smoke-detection scenario, which is not within the test range of the training set. The scene can be used to test the robustness of the smoke detection algorithm, which proves that the algorithm still has a stable detection ability in unfamiliar scenes. From the test results, we can see that both Yolov5s and  
Figure 10: Detection results of different algorithms in autumn. Figure 9: Detection results of different algorithms in spring. [PERSON]+DConv failed to detect smoke. In this case, both [PERSON]+ECA and Yolo-Light could detect smoke correctly. In Figure 12, haze is clearly visible in the scene and the visibility is low. In this case, the algorithm ignores the fuzzy smoke. As a result, both Yolov5s and Yolov5s+ECA were able to detect only one large area of smoke and they missed the smaller area of smoke on the right side of the image; however, [PERSON]+ECA had a higher degree of confidence than Yolov5s. Yolov5s+Dconv did not detect smoke due to computational problems. Yolo-Light successfully detected two smog patches and was unable to handle the blurrier smog in the image. Figure 11: Detection results of different algorithms in an unfamiliar scene. Figure 12: Detection results of different algorithms in low visibility scene. Table 7 shows the comparison results of the actual network performance test. The recognition rate of the network is consistently over 87%, showing that the network has strong generalization ability. The recognition rate of Yolov5s is 90.2%, and that of Yolo-Light is 92.6%. Table 8 shows the comparison of Yolo-Light with Yolov5s and Yolov8s (which is the latest version of the Yolo series) [24], further demonstrating the performance of Yolov5s. The above experimental results show that the improved Yolo-Light network has a higher detection ability when using the straw-burning smoke target dataset.