## 5 Discussion and Conclusions  
In order to solve the problems of inadequate feature utilization and inadequate feature fusion in previous pedestrian detectors, we propose AFFDet. As its name suggests, the original adaptive feature fusion methods are the main contributions of this work. Specifically, AFFDet is a detector that uses our original adaptive multi-level and multi-scale feature fusion methods to get the best features for detection, and our innovative MFFU and MFFM play a vital role in it. Among them, MFFU reflects adaptive multi-scale feature fusions, and MFFM reflects adaptive multi-level feature fusions. In the field of pedestrian detection, the proposed AFFDet has achieved leading detection results in the Caltech benchmark. It is the state-of-the-art one-stage pedestrian detector at present with competitive speed as an  
Figure 6: Comparisons with other methods under IoU = 0.5 setting on Caltech.  
Figure 7: Comparisons with other methods under IoU = 0.75 setting on Caltech.  
anchor-free one-stage approach. According to our experiments, MFFU can be used independently as a feature fusion module, which can improve the model performance. Compared with many feature fusion modules like MFFU which are simply connected together, MFFM structure can further improve the performance. Great performance has been achieved by our proposed detector when only very simple data augmentation techniques are adopted and a small part of samples are used for training due to the limitation of our conditions. Theoretically, training with some powerful data augmentation techniques used in many detection methods and more training samples can further improve the performance. When the number of MFFU in MFFM is 3, it already achieved excellent results, and it does not increase the parameters of the model but only slightly reduces the speed.  
Since Caltech is a dataset recorded by car cameras in the regular traffic scenes in different cities, which is basically consistent with the scenes in real life, the excellent performance of the proposed method on Caltech can basically continue to real life. However, in the real scene, especially in some scenes that need high-resolution detection, the computation of this method is too large, and it is difficult to achieve the speed of real-time detection. The follow-up work will mainly focus on reducing the amount of computation and improving the speed of the model while keeping the current performance basically unchanged or slightly reduced. It will be divided into a high-performance network and a high-efficiency network for high-precision detection scenarios and high-speed detection scenarios to further improve the practicality. We will also verify our method in more fields such as vehicle detection and more datasets.  
Conceptualization, Y.X.; methodology, Y.X. and Q.Y.; software, Y.X.; validation, Y.X.; writing--original draft preparation, Y.X.; writing--review and editing, Y.X. and Q.Y.; supervision, Q.Y.; project administration, Q.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are openly available in Computational Vision at Caltech at [[http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/)]([http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/)), reference number [23].  
The authors declare no conflict of interest.