LeYOLO, New Embedded Architecture for Object Detection

TL;DR

LeYOLO introduces a new lightweight object detection architecture optimized for embedded devices, achieving high accuracy with low parameters and FLOPs, bridging the gap between efficient models and high-performance YOLO architectures.

Contribution

The paper presents LeNeck and LeYOLO, novel frameworks that enhance accuracy and efficiency of object detection models for resource-constrained environments.

Findings

LeYOLO achieves high accuracy comparable to mainstream YOLO models.

LeNeck significantly improves detection accuracy while maintaining inference speed.

Both models are suitable for ultra-low-power devices like microcontrollers.

Abstract

Efficient computation in deep neural networks is crucial for real-time object detection. However, recent advancements primarily result from improved high-performing hardware rather than improving parameters and FLOP efficiency. This is especially evident in the latest YOLO architectures, where speed is prioritized over lightweight design. As a result, object detection models optimized for low-resource environments like microcontrollers have received less attention. For devices with limited computing power, existing solutions primarily rely on SSDLite or combinations of low-parameter classifiers, creating a noticeable gap between YOLO-like architectures and truly efficient lightweight detectors. This raises a key question: Can a model optimized for parameter and FLOP efficiency achieve accuracy levels comparable to mainstream YOLO models? To address this, we introduce two key contributions to object detection models using MSCOCO as a base validation set. First, we propose LeNeck, a general-purpose detection framework that maintains inference speed comparable to SSDLite while significantly improving accuracy and reducing parameter count. Second, we present LeYOLO, an efficient object detection model designed to enhance computational efficiency in YOLO-based architectures. LeYOLO effectively bridges the gap between SSDLite-based detectors and YOLO models, offering high accuracy in a model as compact as MobileNets. Both contributions are particularly well-suited for mobile, embedded, and ultra-low-power devices, including microcontrollers, where computational efficiency is critical.