# An Approach to Incorporating Implicit Knowledge in Object Detection Models
## 1 Introduction  
In deep learning models, sharing a common underlying network structure is a commonly employed approach when simultaneously handling and optimizing multiple distinct yet related tasks [1]. However, due to the varying feature requirements among different tasks, the sharing of the underlying network may lead to information interference between tasks. The object detection task can be characterized as achieving both object classification and object position regression, and these two tasks may mutually influence each other.  
Furthermore, during the training process of deep learning models, when influenced by data quality and the model structure, the model may lose some valuable information relevant to the task. In convolutional neural networks, due to constraints on parameters such as the size and stride of convolutional kernels, certain local information may be discarded or blurred [2]. This information loss is particularly pronounced in deep neural networks, where each layer of convolutional operations may introduce a certain degree of information loss, thereby decreasing the overall performance and generalization capability of the entire network [3].  
To alleviate the impact of information loss in the training process of object detection models, in this paper, we investigate the causes of model information loss by combining the algorithmic principles of single-stage object detection models. We propose a single-stage object detection method based on implicit knowledge.As shown in Figure 1, we present a method for modeling implicit knowledge that is distinguishable from conventional approaches in object detection, such as knowledge distillation or attention mechanisms. Specifically, to parameterize the implicit knowledge in the model, we introduce a set ofdictionaries into the model to extract implicit knowledge. Experimental results on the UA-DETRAC dataset [4] and KITTI dataset [5] demonstrate that our approach performs well in detecting objects in road scenes.  
In summary, our study makes the following contributions:  
1. The causes of information loss in object detection tasks are thoroughly investigated, and the sources of implicit knowledge are fully analyzed;
2. A method to model implicit knowledge through parameter dictionaries is proposed, integrating the principles of dictionary learning. The experimental section validates the different effects of implicit knowledge on various structures of object detection models;
3. An approach is designed to incorporate implicit knowledge into the model structure of object detection, demonstrating, in the experimental results, that the cost of integrating implicit knowledge into the object detection model is negligible.  
The remaining content of this paper is organized as follows. In Section 2, we introduce related work from three aspects: explicit and implicit knowledge, knowledge dictionaries, and the multi-scale structure of object detection models. In Section 3, we discuss the sources of implicit knowledge, theoretically analyze the feasibility of modeling implicit knowledge, and outline the methods for incorporating implicit knowledge into the model based on the structure of object detection models. Experimental validation of the ways in which implicit knowledge affects the model is conducted in Section 4 to confirm the effectiveness of the proposed approach. Finally, Section 5 concludes the paper and discusses future work.