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Performance Evaluation of Lightweight Detection Algorithms on Compact LiDAR-Camera Configurations for Freight Transportation

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  • Guo, Yi

Abstract

Autonomous freight transportation faces deployment barriers due to high-cost sensor configurations. This research evaluates lightweight detection algorithms across three cost-level LiDAR-camera configurations targeting freight logistics. MobileNet-based detectors, EfficientNet-FPN architectures, and sparse convolutional networks are implemented on NVIDIA Jetson AGX Orin platforms. Experimental validation on the nuScenes dataset demonstrates mid-cost configurations achieve 5.2% mAP reduction versus the 64-channel baseline while reducing costs by 85%. Low-cost 16-channel configurations maintain 83.3% detection accuracy at 97% cost reduction. Hardware deployment reveals MobileNet-SSD achieves 28.7 FPS with a 3.2GB memory footprint. Pareto-optimal analysis identifies optimal sensor-algorithm combinations for budget-constrained scenarios, providing quantitative guidance for commercial autonomous trucking deployment.

Suggested Citation

  • Guo, Yi, 2026. "Performance Evaluation of Lightweight Detection Algorithms on Compact LiDAR-Camera Configurations for Freight Transportation," Journal of Science, Innovation & Social Impact, Pinnacle Academic Press, vol. 1(1), pages 398-409.
    • Handle: RePEc:dba:jsisia:v:1:y:2026:i:1:p:398-409
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    File URL: https://pinnaclepubs.com/index.php/JSISI/article/view/950/910
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