Accurate and fast detection of tomatoes based on improved YOLOv5s in natural environments

Uneven illumination, obstruction of leaves or branches, and the overlapping of fruit significantly affect the accuracy of tomato detection by automated harvesting robots in natural environments. In this study, a proficient and accurate algorithm for tomato detection, called SBCS-YOLOv5s, is proposed to address this practical challenge. SBCS-YOLOv5s integrates the SE, BiFPN, CARAFE and Soft-NMS modules into YOLOv5s to enhance the feature expression ability of the model. First, the SE attention module and the C3 module were combined to form the C3SE module, replacing the original C3 module within the YOLOv5s backbone architecture. The SE attention module relies on modeling channel-wise relationships and adaptive re-calibration of feature maps to capture important information, which helps improve feature extraction of the model. Moreover, the SE module's ability to adaptively re-calibrate features can improve the model's robustness to variations in environmental conditions. Next, the conventional PANet multi-scale feature fusion network was replaced with an efficient, weighted Bi-directional Feature Pyramid Network (BiFPN). This adaptation aids the model in determining useful weights for the comprehensive fusion of high-level and bottom-level features. Third, the regular up-sampling operator is replaced by the Content Aware Reassembly of Features (CARAFE) within the neck network. This implementation produces a better feature map that encompasses greater semantic information. In addition, CARAFE's ability to enhance spatial detail helps the model discriminate between closely spaced fruits, especially for tomatoes that overlap heavily, potentially reducing the number of merging detections. Finally, for heightened identification of occluded and overlapped fruits, the conventional Non-Maximum-Suppression (NMS) algorithm was substituted with the Soft-NMS algorithm. Since Soft-NMS adopts a continuous weighting scheme, it is more adaptable to varying object sizes, improving the handling of small or large fruits in the image. Remarkably, this is carried out without introducing changes to the computational complexity. The outcome of the experiments showed that SBCS-YOLOv5s achieved a mean average precision (mAP (0.5:0.95)) of 87.7%, which is 3.5% superior to the original YOLOv5s model. Moreover, SBCS-YOLOv5s has a detection speed of 2.6 ms per image. Compared to other state-of-the-art detection algorithms, SBCS-YOLOv5s performed the best, showing tremendous promise for tomato detection in natural environments.