Precise optical image localization is crucial for improving industrial production efficiency and quality. Traditional image processing and localization methods have low accuracy and are vulnerable to environmental factors such as lighting and noise in complex scenes. Although classical deep learning networks have been widely applied in natural-scene object detection， industrial inspection， grasping， defect detection， and other areas， directly applying pixel-level precise localization to industrial components is still challenging owing to the requirements of massive data training， complex deep learning models， and redundant and imprecise detection boxes. To address these issues， this paper proposes a lightweight deep learning network approach for pixel-level accurate localization of component optical images. The overall design of the network adopts an Encoder–Decoder architecture. The Encoder incorporates a three-level bottleneck cascade to reduce the parameter complexity of feature extraction while enhancing the network’s nonlinearity. The Encoder and Decoder perform feature layer fusion and concatenation， enabling the Encoder to obtain more high-resolution information after upsampling convolution and to reconstruct the original image details more comprehensively. Finally， the weighted Hausdorff distance is utilized to establish the relationship between the Decoder's output layer and the localization coordinates. Experimental results demonstrate that the lightweight deep learning localization network model has a parameter size of 57.4 kB， and the recognition rate for localization accuracy less than or equal to 5 pixels is greater than or equal to 99.5%. Thus， the proposed approach satisfies the requirements of high localization accuracy， high precision， and strong anti-interference capabilities for industrial component localization.