The proposed Shape Recovery Layer (SRLayer) and Self Diffusion Layer (SDLayer) can be enhanced to better manage occlusions and improve performance on small objects through several strategies.
Multi-View Fusion: By integrating features from multiple camera views, the model can gain a more comprehensive understanding of the scene, which is particularly beneficial for occluded objects. This multi-view approach can help in reconstructing the shapes of partially visible objects, allowing the SRLayer to recover more accurate shapes even when parts of the object are hidden.
Contextual Information Utilization: Incorporating contextual information from the surrounding environment can aid in predicting the shapes of occluded objects. For instance, using semantic segmentation maps or depth information from other sensors can provide additional cues for the SRLayer to infer the shapes of small objects that may be obscured.
Adaptive Diffusion Mechanisms: The SDLayer can be modified to include adaptive diffusion strategies that take into account the size and visibility of objects. For small objects, the diffusion process can be fine-tuned to ensure that features are spread more aggressively towards the center, compensating for the lack of visible features due to occlusion.
Occlusion-Aware Loss Functions: Implementing loss functions that specifically penalize mispredictions of occluded objects can guide the training process to focus on improving the detection of small and occluded objects. This can be achieved by emphasizing the importance of correctly predicting the boundaries of these objects during the training phase.
Temporal Information Integration: If the detection system is deployed in a dynamic environment, leveraging temporal information from previous frames can help in predicting the shapes of occluded objects. This can be particularly useful in scenarios where small objects are frequently occluded by larger ones.
By implementing these strategies, the FSMDet framework can enhance its robustness against occlusions and improve its performance in detecting small objects, ultimately leading to more accurate and reliable 3D detection outcomes.