GBForkDet: A Lightweight Object Detector for Forklift Safety Driving - Linhua Ye; Songhang Chen (IEEE Access, 2023)
The importance of object detection in intelligent logistics applications is increasingly recognized. However, current detector models suffer from challenges such as high computational cost and low detection accuracy, which limit their deployment on edge devices with limited computational power in logistics scenarios. To address these issues, this paper proposes a novel lightweight detector model (GBForkDet) based on YOLOv8 for forklift safety driving. Firstly, the Ghost module is integrated into YOLOv8 to optimize the Backbone feature extraction process, reducing the computational cost of the model. Then, a Bi-directional Omni-Dimensional Dynamic Neck (BiODNeck) is designed to fuse feature information in complex logistics scenarios. GBForkDet significantly improves the capture of contextual logistics background information by reconstructing the Neck of YOLOv8 with BiODNeck. This is attributed to cross-layer weighted feature fusion and a complementary focus on learning convolutional kernels in any convolutional layer along all four dimensions of the kernel space. Furthermore, the introduction of the Normalized Wasserstein distance (NWD) as an enhanced loss function improves the detection of small distant objects in logistics scenarios. Experimental results show that GBForkDet achieves a mAP of 92.7% and 95.3% on the established Forklift-3k and KITTI datasets while reducing the model parameters by 17.9% and the computational cost by 22.5% compared to the baseline YOLOv8s model. Under the Jetson Nano edge platform and $640× 640$ input size, the GBForkDet model achieves a remarkable inference time of 108.2 ms using TensorRT acceleration.