Abstract
To address issues such as slow response speed and low detection accuracy in fallen walnut picking robots in complex orchard environments, this paper proposes a detection and path planning method that integrates star-shaped lightweight convolution with NDT-RRT. The method includes the improved lightweight detection model YOLO-FW and an efficient path planning algorithm NDT-RRT. YOLO-FW enhances feature extraction by integrating star-shaped convolution (Star Blocks) and the C3K2 module in the backbone network, while the introduction of a multi-level scale pyramid structure (CA_HSFPN) in the neck network improves multi-scale feature fusion. Additionally, the loss function is replaced with the PIoU loss, which incorporates the concept of Inner-IoU, thus improving regression accuracy while maintaining the model's lightweight nature. The NDT-RRT path planning algorithm builds upon the RRT algorithm by employing node rejection strategies, dynamic step-size adjustment, and target-bias sampling, which reduces planning time while maintaining path quality. Experiments show that, compared to the baseline model, the YOLO-FW model achieves precision, recall, and mAP@0.5 of 90.6%, 90.4%, and 95.7%, respectively, with a volume of only 3.62 MB and a 30.65% reduction in the number of parameters. The NDT-RRT algorithm reduces search time by 87.71% under conditions of relatively optimal paths. Furthermore, a detection and planning system was developed based on the PySide6 framework on an NVIDIA Jetson Xavier NX embedded device. On-site testing demonstrated that the system exhibits good robustness, high precision, and real-time performance in real orchard environments, providing an effective technological reference for the intelligent operation of fallen walnut picking robots.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 7
Claim · model-written summary
YOLO-FW achieved 90.6% precision against the baseline.
Verbatim
e0“compared to the baseline model, the YOLO-FW model achieves precision, recall, and mAP@0.5 of 90.6%, 90.4%, and 95.7%, respectively”
t0“Walnut”
Claim · model-written summary
YOLO-FW achieved 90.4% recall against the baseline.
Verbatim
e0“compared to the baseline model, the YOLO-FW model achieves precision, recall, and mAP@0.5 of 90.6%, 90.4%, and 95.7%, respectively”
t0“Walnut”
Claim · model-written summary
YOLO-FW achieved 95.7% mAP@0.5 against the baseline.
Verbatim
e0“compared to the baseline model, the YOLO-FW model achieves precision, recall, and mAP@0.5 of 90.6%, 90.4%, and 95.7%, respectively”
t0“Walnut”
Claim · model-written summary
YOLO-FW had a volume of 3.62 MB.
Verbatim
e0“with a volume of only 3.62 MB and a 30.65% reduction in the number of parameters”
t0“Walnut”
Claim · model-written summary
YOLO-FW reduced parameter count by 30.65%.
Verbatim
e0“a 30.65% reduction in the number of parameters”
t0“Walnut”
Claim · model-written summary
NDT-RRT reduced search time by 87.71% with relatively optimal paths.
Verbatim
e0“The NDT-RRT algorithm reduces search time by 87.71% under conditions of relatively optimal paths”
t0“Walnut”
Claim · model-written summary
On-site testing found the system robust, precise, and real-time in orchards.
- Taxon
- Walnutt0
- Outcome
- good robustness, high precision, and real-time performancee0
- Setting
- real orchard environmentse0
Not reportedComparator
Verbatim
e0“On-site testing demonstrated that the system exhibits good robustness, high precision, and real-time performance in real orchard environments”
t0“Walnut”