Year: 2025
Type: article
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 posttraining quantization. On the LOCO warehouse dataset—comprising 10,000 images of five classes under variable lighting, occlusion, and scale—SmartNavNet achieves 62.2% [email protected], 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
Author: Manodharshan K
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410117457