SB-YOLO-V8: A Multilayered Deep Learning Approach for Real-Time Human Detection

Over the past decade, significant advancements in computer vision have been made, primarily driven by deep learning-based algorithms for object detection. However, these models often require large amounts of labeled data, leading to performance degradation when applied to tasks with limited data sets, particularly in scenarios involving moving objects. For instance, real-time detection and detection of humans in agricultural settings pose challenges that demand sophisticated vision algorithms. To address this issue, we propose SB-YOLO-V8 (Scene-Based—You Only Look Once—Version 8), an optimized YOLO-based convolutional neural network (CNN) designed specifically for real-time human detection, not just limited to citrus farms but applicable to various agricultural and urban environments. The versatility of SB-YOLO-V8 is underpinned by its robust handling of class imbalances and enhanced feature extraction through binary adaptive learning optimization (ALO) and Synthetic Minority Over-sampling Technique (SMOTE), addressing common challenges like limited labeled data sets and varied object dynamics in different scenarios. The proposed method is trained using images and videos of human workers captured by autonomous farm equipment. Evaluation metrics, including frame per second (FPS), model performance, and efficiency, demonstrate that the proposed method outperforms variances of YOLO such as YOLO-V8, YOLO-V7, YOLO-V6, YOLO-V4, and YOLO-V3 with an average FPS of 13.63 and a precision of 91%. In effect, the proposed SB-YOLO-V8 presents an efficient solution for real-time human detection in challenging visual scenarios.