SoftFusion: A Low-Cost Approach to Enhance Reliability of Object Detection Applications

DNNs are deployed in mission critical applications such as object detection in autonomous vehicles (AVs). Ensuring the reliability of detections made by these models plays an essential role in robustness and practicality of these systems. By analyzing traditional object detectors, we observe a significant number of objects being missed by the detector, which is a problem for safe deployment of AVs. Traditionally, sensor fusion wherein detections from multiple sensors (camera, LiDAR, etc.) are combined to create a robust detection system is utilized, but suffers from high cost and computing overheads. To attack this seemingly necessary tradeoff, we propose SoftFusion in this paper. SoftFusion introduces the concept of intra-sensor fusion wherein a diversity of inputs is synthesized during runtime and can be efficiently evaluated by the original object detector. The predictions are then intelligently combined to realize robust detections over the baseline detector but at a fraction of the cost of traditional fusion. SoftFusion is evaluated across 7 different benchmarks over 2 different datasets, and is shown to achieve 3.45% gain in average precision with less than 23% latency overhead. As context for these results, prior research demonstrates that hard fusion of camera and LiDAR provides on average 4.02% gain in average precision in similar tasks with an overhead of approximately up to 2x computation.