POD PEPPER TARGET DETECTION BASED ON IMPROVED YOLOv8

Pod pepper (Capsicum annuum var. conoides), a common variety of chili pepper, poses a challenge for traditional object detection methods due to its complex morphological features and diverse types. This study focuses on the application of machine vision technology to address the issue of pod pepper object detection. Firstly, a large number of pod pepper sample images were collected, followed by data preprocessing and annotation. Subsequently, YOLOv3, YOLOv5, YOLOv6, and YOLOv8 pod pepper object detection models were established, with YOLOv8 yielding the best detection results with a mean Average Precision (mAP) value of 81.6%. Next, different attention mechanisms were incorporated into the YOLOv8 network structure, with experimental results indicating that the Triplet Attention mechanism performed the best in pod pepper object detection, achieving an mAP value of 82.5%, a 0.9% improvement over YOLOv8. To further optimize the effectiveness of the attention mechanisms, Triplet Attention was added at different positions within the YOLOv8 network. The experiment showed that the location of adding the attention mechanism significantly impacted the pod pepper detection results. When Triplet Attention was added at the 5th layer, the best detection performance was achieved, with an mAP value of 84.1%, a 2.5% improvement over the original YOLOv8. This research provides technical support for intelligent harvesting of pod pepper.