YOLOMuskmelon: Quest for Fruit Detection Speed and Accuracy Using Deep Learning

Abstract

Fruit detection plays a vital role in harvesting robot platforms. However, complicated environment attributes such as illumination variation, occlusion, have made fruit detection a challenging task. A robust YOLOMuskmelon model that is accurate and fast was proposed to solve detection difficulties. The YOLOMuskmelon model incorporated ReLU activated ResNet43 backbone with new 2,3,4,3,2 residual block arrangement, spatial pyramid pooling(SPP), complete Intersection over Union (CIoU) loss, feature pyramid network(FPN), and distance Intersection over Union-Non Maximum Suppression(DIoU-NMS) to improve detection performance. The obtained average precision (AP) results of YOLOMuskmelon at 89.6% is greater than YOLOv3 at 82.3%, YOLOResNet50 at 85.5%, but less than YOLOv4 at 91.6%. However, the detection speed of YOLOMuskmelon at 96.3 frame per second(fps) outperformed YOLOv3 at 56.6fps, YOLOv4 at 54.1fps and YOLOResNet50 at 71.2fps. Meanwhile, the YOLOMuskmelon which is 56.1% faster than YOLOv4 model showed a better generalization and real-time fruit harvesting robots prospect.

Keywords

• YOLOMuskmelon
• fruit detection
• speed and accuracy
• complicated environment
• harvesting robots