Reduce Detection Latency of YOLOv5 to Prevent Real-Time Tracking Failures for Lightweight Robots

Lightweight robots are frequently engaged in real-time tracking tasks to provide human companionship services. For effective target tracking, the YOLO series is often employed as a lightweight object detection framework in robot systems. However, YOLO still demands substantial resources to train larger-scale models, striking a balance between accuracy and resource efficiency. Deploying YOLO directly on robots with limited computing resources can lead to significant delays in detection, compromising the effectiveness of tracking tasks. A deeper concern arises from the prevalent use of CPUs as the primary computing units in robots, rendering many existing model optimization techniques, which primarily target GPU computing, unsuitable for this context.

To tackle this challenge, we propose a novel detection framework called SLCNet-YOLOv5, specifically designed for deployment in CPU-centric computing environments on robots. The core concept of SLCNet-YOLOv5 entails substituting the native YOLOv5 backbone network with SLCNet, which is a simplified version derived from the existing CPU convolutional neural network, PP-LCNet. It is important to note that our aim is not to enhance PP-LCNet to improve inference accuracy but rather to simplify it to enhance inference speed, while tolerating a certain degree of accuracy loss. This is because excessive inference latency may lead to real-time tracking failures. By employing a backbone network optimized for CPU-centric computation and reducing the computational complexity of the detection model, SLCNet markedly reduces latency, expediting the detection process, with only a minor trade-off in accuracy. In comparison to the performance of the state-of-the-art detector YOLOv5, experimental results on publicly available coco-foot-and-leg and PASCAL VOC datasets demonstrate significant enhancements in detection speed per image on CPU-centric terminals, with respective increases of 62.8% and 81.3%, alongside marginal declines in mean Average Precision (mAP) at 0.5 Intersection over Union (IoU) threshold, with losses of 0.077 and 0.165.