Identification of Smoke from Straw Burning in Remote Sensing Images with the Improved YOLOv5s Algorithm

Abstract

Controlling straw burning is important for ensuring the ambient air quality and for sustainable agriculture. Detecting burning straw is vital for managing and controlling straw burning. Existing methods for detecting straw combustion mainly look for combustion products, especially smoke. In this study, the improved You Only Look Once version 5 (YOLOv5s) algorithm was used to detect smoke in Sentinel-2 images captured by remote sensing. Although the original YOLOv5s model had a faster detection speed, its detection accuracy was poor. Thus, a convolutional block attention module was added to the original model. In addition, in order to speed up the convergence of the model, this study replaced the leaky Rectified Linear Unit (leaky ReLU) activation function with the Mish activation function. The accuracy of the improved model was approximately 4% higher for the same detection speed. The improved YOLOv5s had a higher detection accuracy and speed compared to common target detection algorithms, such as RetinaNet, mask Region-Based Convolutional Neural Network (R-CNN), Single-Shot Multibox Detector (SSD), and faster R-CNN. The improved YOLOv5s analyzed an image in 2 ms. In addition, mAP50 exceeded 94%, demonstrating that with this study’s improved method, smoke can be quickly and accurately identified. This work may serve as a reference for improving smoke detection, and for the effective management and control of straw burning.

Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds

“…The organic integration of these two tasks effectively enhances the model's ability to recognize real wildfire smoke. However, due to the traditional separation of the two tasks, there was no dataset combining both tasks in practice [22][23][24]. This prompted us to collect and integrate various wildfire data, forming the MODIS_Smoke_FPT dataset that includes both research tasks.…”

“…However, clouds share similar shapes and properties with wildfire smoke, often leading to misidentification between smoke and clouds in large-scale detection [23]. Smoke and clouds both have low transparency, obscuring ground features in remote sensing images and making it difficult to distinguish ground features [24][25][26][27].…”

Detection of Smoke from Straw Burning Using Sentinel-2 Satellite Data and an Improved YOLOv5s Algorithm

“…Therefore, this area is a prominent grain-producing region in Jilin Province with a substantial commercial grain base [29] and makes an outstanding contribution to ensuring food security and stable social development in China. According to the Yearbook of Jilin Province in 2020 [30], the main crop types producing straw in the study area include maize, rice, and soybeans, with a total sown area of 3.12 million hectares, 0.49 million hectares, and 0.12 million hectares for these three crops, respectively. The annual production of straw in Jilin Province exceeds 40 million tons [31], and the straw resource output in the study area reached 10.87 t/hm 2 , which clearly is a large amount of straw.…”

Yolo-Light: Remote Straw-Burning Smoke Detection Based on Depthwise Separable Convolution and Channel Attention Mechanisms

“…Liu 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.…”