In traditional target detection methods,there is a trade-off that exists between target detection accuracy and real-time detection, and the recognition accuracy is inferior under actual, complex production scenarios. To address this, a deep learning detection method based on the Inception-SSD framework was herein proposed.In this framework, an inception network structure was introduced into the extra layer of the SSD network, and batch normalization (BN) and residual structure connection were used to capture target information without increasing network complexity. Owing to this, detection accuracy was improved without the real-time detection performance being affected and the algorithm also becomes more robust. Subsequently, the exclusion loss term based on the original loss function increases, which in turn improves the loss function. Furthermore, a non-maximum suppression weighting method was used to overcome the shortcomings of insufficient expression ability of the model. Finally, the improved SSD algorithm was trained and tested on a self-made dataset and compared with the original and the latest inception-SSD algorithms.Experimental results show that the detection accuracy of the proposed method is 97.8% in an actual production process, which is an improvement of 11.7 percentage points over the original SSD algorithm, and the detection speed is 41 fps. Therefore, the proposed method exhibits superior real-time performance, thereby meeting actual production demands.