Efficiently and accurately separating infrared (IR) small targets from complex backgrounds presents a significant challenge. Numerous studies in the literature have proposed various feature fusion modules designed specifically to enhance the extraction of IR small target features. While these designs offer some incremental improvement to the accuracy of IR small target detection, they come at a steep cost of significantly increasing network parameters and FLOPs. Striving for a balance between computational efficiency and model accuracy, we decided to forgo these complex feature fusion modules. Instead, we developed a new lightweight encoding and decoding structure known as the Lightweight IR Small Target Segmentation Network (LW-IRSTNet). This structure integrates regular convolutions, depthwise separable convolutions, atrous convolutions, and asymmetric convolutions modules. In addition, we devised post-processing modules including an eight-neighborhood clustering algorithm and an online target feature adjustment strategy. Experimental results indicate that: 1) the segmentation accuracy metrics of LW-IRSTNet match the best results of 14 state-of-the-art comparative baselines; 2) the parameters and FLOPs of LW-IRSTNet, at only 0.16M and 303M respectively, are significantly smaller in comparison to these baselines; and 3) the post-processing modules enhance both user-friendliness and the robustness of algorithm deployment. Moreover, LW-IRSTNet has been successfully implemented on both embedded platforms and websites, expanding its range of applications. Utilizing the ONNX framework, NPU acceleration, and CPU multi-threaded resource allocation, we have been able to achieve high-performance inference capabilities, as well as online dynamic threshold adjustment with the LW-IRSTNet. The source codes for this project can be accessed at https://github.com/kourenke/LW-IRSTNet.