Research on lightweight detection methods for the golden snub-nosed monkey based on YOLOv5n

To enable rapid detection of golden snub-nosed monkeys in complex environments, reduce the human costs associated with tracking and observing these monkeys, and accelerate the development of intelligent forest monitoring, we propose the PCB-YOLOv5n-prune model. This model is designed for lightweight devices and is based on channel pruning and module reconstruction. First, we constructed a dataset that combines annotations of the golden snub-nosed monkey's face and body, with some data converted to grayscale. We mixed and expanded five data styles to decrease reliance on color and enhance the informational content. Next, we applied the Sparse Group Lasso selection operator method to slim down the YOLOv5n primitive model for golden snub-nosed monkey detection, improving the detection speed of the underlying network. We then introduced a lightweight convolutional module, PConv, to create the improved residual branching module, CPB, which reduces model computation and memory access. Additionally, we incorporated a lightweight attention module, ECA, to adaptively weight channel features, facilitating local cross-channel information interaction. Finally, we integrated the ByteTrack multi-target tracking algorithm to enable continuous tracking of golden snub-nosed monkeys and visualize detection results. Experimental results demonstrate that the PCB-YOLOv5n-prune model reduces the number of parameters, floating point operations, and model weight by 61 %, 56 %, and 55 %, respectively, compared to the original YOLOv5n model, while significantly improving detection speed.