# Iae-km3D a 3D Object Detection Method Based on an Improved KM3D Network
## 3 Experimental Results and Analysis
### Ablation Experiment and Analysis  
In this paper, the ablation experiment is used to verify the effect of each improvement module under the premise of keeping the environment and parameters consistent. The improvement point ablation experiments are divided into ten groups, with KM3D as the benchmark model, and \"\  
Based on Table 7, the following conclusions can be drawn:  
1. Model 1 is the KM3D benchmark network and is a comparison benchmark for subsequent experiments. Its AP\({}_{\text{2D}}\) is 82.95%, its AP\({}_{\text{3D}}\) is 36.39%, its AP\({}_{\text{BEV}}\) is 42.31%, and its time is 0.030S. 2. Model 2 uses the residual module of Resnet-V2, which is more trainable and generalizable than Model 1. The accuracy is also improved. 3. Model 3 is based on model 1, with the addition of the IN module, which continues to improve their performance without increasing computational costs, based on accuracy and more significant improvement. 4. Model 4 introduces the Simam mechanism based on model 1. It allows the network to focus on more key features and does not increase the network complexity. On this basis, accuracy and more significant improvement are needed. 5. Model 5 is based on model 1; the circular Gaussian kernel is improved to an elliptical Gaussian kernel, which enhances the algorithm's ability to detect targets. On this basis, accuracy and more significant improvement are needed. 6. Model 6 is based on model 1, adding a key point loss function based on the predicted values of the key point to improve the training ratio of the algorithm for complex samples. On this basis, accuracy and more significant improvement are needed. 7. Model 7 is based on model 1, to which the residual module of Resnet-V2 was sequentially applied, and the IN module was added. This model improves the evaluation index for Model 1 and Model 2, including accuracy and more significant improvement. 8. Model 8 is based on model 1, to which the residual module of Resnet-V2 was sequentially applied, the IN module was added, and the Simam mechanism was introduced. This model improves the evaluation index for models 1, 2, and 3: accuracy and more significant improvement. 9. Model 9 adopts the residual module of Resnet-V2, the IN module, and introduces the Simam mechanism based on Model 1, in that order. The circular Gaussian kernel is improved to an elliptical Gaussian kernel. The model improves the evaluation index for Model 1, Model 2, Model 3, and 4: accuracy and significant improvement. 10. Model 10 is the final improved algorithm of this paper, which adopts the residual module of Resnetv2, adds the IN module, and introduces the Simam mechanism. It improves the circular Gaussian kernel to an elliptical Gaussian kernel and incorporates a key point loss function based on the predicted values of key points. Compared with Model 1, it improves 5%, 12.5%, and 8.3% in AP\({}_{\text{2D}}\), AP\({}_{\text{3D}}\), and AP\({}_{\text{BEV}}\), respectively. The experiments show that the algorithm in this paper, compared with the original KM3D algorithm, has dramatically improved in all evaluation indexes, improved the detection accuracy, and satisfied the demand for real-time detection. Relative to Model 2, there is an improvement of 4.3%, 12%, and 7.6% in AP\({}_{\text{2D}}\), AP\({}_{\text{3D}}\), and AP\({}_{\text{BEV}}\), respectively. Model 10 is based on Model 2 with the addition of the IN module and the introduction of the Simam mechanism. The circular Gaussian kernel is improved to an elliptical Gaussian kernel. A key point loss function based on the predicted values of key points is added. Although it increases the complexity of the model calculation, the detection accuracy is significantly improved as the detection speed FPS is reduced. Relative to Model 3, there is an improvement of 3.8%, 9.7% and 5.7% in AP\({}_{\text{2D}}\), AP\({}_{\text{3D}}\), and AP\({}_{\text{BEV}}\), respectively. Model 10 introduces the Simam mechanism based on Model 3 by using the residual module of Resnet-V2.