SmartNavNet: A Lightweight Object Detection Model for Warehouse Automation

Abstract

Warehouse automation demands fast, accurate object detection capable of operating on resource-constrained edge devices. In this work, we present SmartNavNet, a purpose-built detection network that synergistically integrates Ghost Convolution modules, a CSP-PANet feature aggregation neck, Squeeze-and-Excitation channel attention, and INT8 post-training quantization. On the LOCO warehouse dataset—comprising 10,000 images of five classes under variable lighting, occlusion, and scale—SmartNavNet achieves 62.2% mAP@0.5, 70.2% precision, and 55.7% recall, with a 10?ms average inference latency on a Snapdragon 855 and a 3?MB quantized footprint. Compared to YOLOv4-Tiny and YOLOv5n baselines, our model offers up to 4.2% mAP improvement while halving model size and reducing latency by 20%, making it uniquely suitable for real-time warehouse applications.

Introduction

The rapid growth of e-commerce demands highly efficient and accurate warehouse object detection systems that can run on low-power edge devices. Existing high-capacity convolutional networks provide strong accuracy but are too resource-intensive, while lightweight detectors like YOLOv4-Tiny and YOLOv5n trade off accuracy in complex warehouse scenes.

To address this, the authors propose SmartNavNet, a compact, efficient, and accurate model optimized for warehouse imagery. It incorporates four key innovations:

1. Ghost Convolution modules for efficient feature generation by producing intrinsic feature maps and inexpensive “ghost” maps, reducing computation and parameters.

2. CSP-PANet neck for multi-scale feature fusion that handles objects of varying sizes by combining cross-stage partial connections with path aggregation.

3. Squeeze-and-Excitation (SE) blocks for adaptive channel attention, emphasizing important features and improving accuracy under occlusion and varying lighting.

4. INT8 quantization to compress the model and enable fast real-time inference on edge devices like Snapdragon 855 with minimal accuracy loss.

SmartNavNet’s architecture uses a Ghost-MobileNetV3 backbone, a CSP-PANet neck with SE blocks, and a multi-scale detection head targeting common warehouse objects such as boxes, pallets, forklifts, and workers.

Trained and evaluated on the LOCO warehouse dataset, SmartNavNet achieves higher mean average precision (62.2% mAP@0.5) than YOLOv4-Tiny and YOLOv5n, with smaller model size (3 MB) and lower latency (10 ms), making it well-suited for deployment on resource-constrained hardware.

Conclusion

In this paper, we introduced SmartNavNet, a lightweight object detection model meticulously engineered for real-time warehouse automation on resource-constrained edge devices. By integrating Ghost Convolution modules for efficient feature generation, a CSP-PANet neck for robust multi-scale fusion, Squeeze-and-Excitation blocks for adaptive channel attention, and INT8 post-training quantization for deployment-ready compression, SmartNavNet strikes an optimal balance between detection accuracy, inference speed, and model size. Our extensive evaluation on the LOCO dataset, encompassing 10?000 images under diverse lighting, occlusion, and scale conditions, demonstrates that SmartNavNet achieves 62.2% mAP@0.5, 70.2% precision, and 55.7% recall, with an average inference latency of 10?ms on a Snapdragon?855 and a compact 3?MB quantized footprint. Compared to established lightweight baselines—YOLOv4-Tiny and YOLOv5n—our model delivers up to 4.2% higher mAP, reduces model size by more than 50%, and accelerates inference by 20%, affirming its suitability for real-time monitoring and robotic guidance in modern warehouses. A. Key takeaways 1) Efficiency Gains: Ghost Convolutions and CSP-PANet reduce computational overhead without compromising feature richness. 2) Attention Benefits: SE blocks enhance discriminative power, particularly in cluttered scenes. 3) Edge Deployment: INT8 quantization enables sub-10?ms inference with minimal accuracy loss. B. Future Directions 1) Recall Improvement: Incorporating advanced augmentation strategies (e.g., mosaic, CutMix) and robust loss functions (e.g., GIoU, DIoU) to further boost recall on heavily occluded objects. 2) 3D Integration: Extending the 2D detector with depth or stereo vision inputs for enhanced robotic manipulation and obstacle avoidance. 3) Hardware-Aware Optimization: Employing Neural Architecture Search (NAS) techniques tailored to specific SoCs and microcontrollers to maximize throughput and energy efficiency. Through these enhancements, SmartNavNet lays a foundation for highly responsive, accurate, and compact vision systems in next-generation warehouse automation.

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Copyright

Copyright © 2025 Manodharshan K, Valliappan V, Tamil K, Javith Ali S, Dr. A. Geetha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.