Three-Dimensional Millimeter-Wave Object Detector Based on the Enhancement of Local-Global Contextual Information

Millimeter-wave (MMW) point clouds, characterized by their low resolution and high noise, limit the detection accuracy of point-based IA-SSD method due to the inadequate consideration of contextual information in MMW scenarios. Therefore, this paper proposes a three-dimensional (3D) MMW object detector, greatly augmenting the detection performance of the baseline model IA-SSD by the integration of the local-global context information. Central to our approach is the implementation of a multi-scale feature aggregation (MFA) module in the encoder stage of IA-SSD, which utilizes a self-attention mechanism to apprehend local contextual distinctions. This module is further applied to the centroid aggregation stage to enhance the capture of local context from foreground points. Complementarily, a global feature fusion module is devised to combine global contextual insights, drawing upon the localized information delineated by the MFA modules. This integrated framework significantly diminishes the false detection rate while concurrently elevating the detection precision for occluded objects. Relative to the IA-SSD baseline, the empirical evaluations validate the efficiency of our proposed model, demonstrating marked decreases in false positives and false negatives. Specifically, there is a 2.78% and 7.39% improvement in AP_R40_0.25 and AP_R40_0.5, respectively. When the intersection-over-union threshold is set as 0.25 and 0.5, the corresponding recall rate increases by 2.13% and 6.2%, respectively. Moreover, the inference speed reaches 32.3 frames per second(FPS), only a slight decrease of 2.9 FPS compared to the baseline model. These results demonstrate that the proposed detector significantly enhances detection performance without compromising on speed, marking a considerable advancement in the domain of 3D MMW object detection.