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The frequent occurrence of forest fires in recent years has not only seriously damaged the forests’ ecological environments but also threatened the safety of public life and property. Smoke, as the main manifestation of the flame before it is produced, has the advantage of a wide diffusion range that is not easily obscured. Therefore, timely detection of forest fire smoke with better real-time detection for early warnings of forest fires wins valuable time for timely firefighting and also has great significance and applications for the development of forest fire detection systems. However, existing forest fire smoke detection methods still have problems, such as low detection accuracy, slow detection speed, and difficulty detecting smoke from small targets. In order to solve the aforementioned problems and further achieve higher accuracy in detection, this paper proposes an improved, new, high-accuracy forest fire detection model, the OBDS. Firstly, to address the problem of insufficient extraction of effective features of forest fire smoke in complex forest environments, this paper introduces the SimAM attention mechanism, which makes the model pay more attention to the feature information of forest fire smoke and suppresses the interference of non-targeted background information. Moreover, this paper introduces Omni-Dimensional Dynamic Convolution instead of static convolution and adaptively and dynamically adjusts the weights of the convolution kernel, which enables the network to better extract the key features of forest fire smoke of different shapes and sizes. In addition, to address the problem that traditional convolutional neural networks are not …