# Iae-km3D a 3D Object Detection Method Based on an Improved KM3D Network
## 4 Conclusions  
Deep learning-based 3D target detection methods need to solve the problem of insufficient 3D target detection accuracy. This paper proposes an IAE-KM3D network algorithm based on the KM3D network. Firstly, the Resnet-V2 network is introduced to redesign the residual module so that the new residual module is more accessible to train and more generalized than before. Then, IBN-NET is introduced to integrate IN and BN as building blocks carefully to improve their performance continuously without increasing computational costs. The introduction of [PERSON]'s parameter-free attention mechanism allows the network to focus on more key features and does not increase the network complexity. After that, the Gaussian kernel is improved so that the heat map generated by KM3D is improved from a fixed circle to an ellipse that varies with the shape of the 3D target, which enhances the algorithm's ability to detect 3D targets. Finally, a key point loss function based on the predicted values of key points is proposed to improve the training ratio of the algorithm for complex samples. Experiments conducted with the KITTI dataset lead to the following conclusions: Compared with the original KM3D network, the IAE-KM3D network model has significantly improved detection accuracy, and the detection performance is better than that of the KM3D algorithm. The effectiveness of the proposed algorithm in this paper verifies the good detection accuracy and speed of 3D target detection using only one monocular camera.  
In the future, the network structure should continue to be optimized to further improve the accuracy of detection of various complex and diverse 3D objects, and the complexity of the network model should be further optimized to obtain higher real-time performance and adapt to the complex and changing driving environment of self-driving cars.  
Methodology, Y.L.; Software, H.W.; Formal analysis, Y.L.; Investigation, H.W., B.T. and Y.L.; Resources, B.T.; Data curation, S.L.; Writing--original draft, S.L.; Writing--review & editing, S.L.; Supervision, Y.S.; Project administration, Y.S.; Funding acquisition, Y.S., H.W. and B.T. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **AP\({}_{\text{3D}}\)** & **AP\({}_{\text{BEV}}\)** & **Time** & **Parameters** \\ \hline Monn3D & 19.49\% & 23.97\% & 4.4 s & 24 M \\
3 DOP & 36.87\% & 43.56\% & 3.2 s & 21 M \\ GS3D & 29.34\% & - & 2.5 s & 42.53 M \\ FQNet & 22.94 & 26.1\% & 3.4 s & 24.9 M \\ IAE-KM3D & 40.94\% & 45.84\% & 0.035 s & 21.3 M \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative experiment.  
**Funding:** This work is supported by the Natural Science Foundation of Hebei Province (Grant No. F 2021402011).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Jizhong Energy Fengfeng Group Company Limited Mechanical and Electrical Department. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.