Automatic detection of disassembly target is the key to automatic disassembly. Aiming at the problems of large parameters number of disassembly target automatic detection algorithm based on deep neural network algorithm and difficult model deployment, a target component intelligent detection method based on lightweight YOLOX-Nano network was proposed. The data set was constructed with the Phillips screw as the object; a YOLOX-Nano network training method based on transfer learning was proposed. Based on the experimental method, the influence law of bounding box regression loss and object confidence loss on network detection accuracy was analyzed, and the optimal combination of target box regression loss and target confidence loss was determined to realize the optimization of network detection accuracy. Finally, taking a certain brand power strip as an example, the proposed method was tested and verified. The results show that using the lightweight network to realize the Phillips screw detection not only obtains a relatively ideal detection effect, but also greatly reduces the deployment time of the model, and also provides an experimental basis for deploying other lightweight networks for target detection.