{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:45:26Z","timestamp":1760150726427,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T00:00:00Z","timestamp":1642377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Study on fruit damage mechanism and tooth comb - air flow harvesting method of Zanthoxylum pepper in mechanized Harvesting","award":["51765003"],"award-info":[{"award-number":["51765003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The target recognition algorithm is one of the core technologies of Zanthoxylum pepper-picking robots. However, most existing detection algorithms cannot effectively detect Zanthoxylum fruit covered by branches, leaves and other fruits in natural scenes. To improve the work efficiency and adaptability of the Zanthoxylum-picking robot in natural environments, and to recognize and detect fruits in complex environments under different lighting conditions, this paper presents a Zanthoxylum-picking-robot target detection method based on improved YOLOv5s. Firstly, an improved CBF module based on the CBH module in the backbone is raised to improve the detection accuracy. Secondly, the Specter module based on CBF is presented to replace the bottleneck CSP module, which improves the speed of detection with a lightweight structure. Finally, the Zanthoxylum fruit algorithm is checked by the improved YOLOv5 framework, and the differences in detection between YOLOv3, YOLOv4 and YOLOv5 are analyzed and evaluated. Through these improvements, the recall rate, recognition accuracy and mAP of the YOLOv5s are 4.19%, 28.7% and 14.8% higher than those of the original YOLOv5s, YOLOv3 and YOLOv4 models, respectively. Furthermore, the model is transferred to the computing platform of the robot with the cutting-edge NVIDIA Jetson TX2 device. Several experiments are implemented on the TX2, yielding an average time of inference of 0.072, with an average GPU load in 30 s of 20.11%. This method can provide technical support for pepper-picking robots to detect multiple pepper fruits in real time.<\/jats:p>","DOI":"10.3390\/s22020682","type":"journal-article","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T20:49:21Z","timestamp":1642452561000},"page":"682","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["A Real-Time Zanthoxylum Target Detection Method for an Intelligent Picking Robot under a Complex Background, Based on an Improved YOLOv5s Architecture"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6331-6940","authenticated-orcid":false,"given":"Zhibo","family":"Xu","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaopeng","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haobo","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangxin","family":"Wan","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Padilla, R., Netto, S.L., and da Silva, E.A.B. (2020, January 1\u20133). A Survey on Performance Metrics for Object-Detection Algorithms. Proceedings of the 2020 International Conference on Systems, Signals and Image Processing, Niteroi, Brazil.","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, J., Li, Y., and Zhao, J. (2019, January 28\u201330). X-ray of Tire Defects Detection via Modified Faster R-CNN. Proceedings of the 2019 2nd International Conference on Safety Produce Informatization, Chongqing, China.","DOI":"10.1109\/IICSPI48186.2019.9095873"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_4","first-page":"51","article-title":"FEB-Stacking and FEB-DNN Models for Stock Trend Prediction: A Performance Analysis for Pre and Post COVID-19 Periods","volume":"4","author":"Ghosh","year":"2021","journal-title":"Appl. Manag. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Malinda, M., and Chen, J. (2021). The forecasting of consumer exchange-traded funds (ETFs) via grey relational analysis (GRA) and artificial neural network (ANN). Empir. Econ.","DOI":"10.1007\/s00181-021-02039-x"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1155\/2021\/2565500","article-title":"A Defect Detection Method for Rail Surface and Fasteners Based on Deep Convolutional Neural Network","volume":"2021","author":"Zheng","year":"2021","journal-title":"Comput. Intel. Neurosc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1155\/2021\/9984787","article-title":"A Novel Neural Network Model for Traffic Sign Detection and Recognition under Extreme Conditions","volume":"2021","author":"Wan","year":"2021","journal-title":"J. Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tang, G., Zhuge, Y., Claramunt, C., and Men, S. (2021). N-YOLO: A SAR Ship Detection Using Noise-Classifying and Complete-Target Extraction. Remote Sens., 13.","DOI":"10.3390\/rs13050871"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, B., and Huang, F. (2021). A Lightweight Deep Network for Defect Detection of Insert Molding Based on X-ray Imaging. Sensors, 21.","DOI":"10.3390\/s21165612"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8846955","DOI":"10.1155\/2020\/8846955","article-title":"A 3D Image Reconstruction Model for Long Tunnel Geological Estimation","volume":"2020","author":"Liu","year":"2020","journal-title":"J. Adv. Transp."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kerkech, M., Hafiane, A., and Canals, R. (2020). VddNet: Vine disease detection network based on multispectral images and depth map. Remote Sens., 12.","DOI":"10.3390\/rs12203305"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Afzaal, H., Farooque, A.A., Schumann, A.W., Hussain, N., McKenzie-Gopsill, A., Esau, T., Abbas, F., and Acharya, B. (2021). Detection of a potato disease (early blight) using artificial intelligence. Remote Sens., 13.","DOI":"10.3390\/rs13030411"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1016\/j.compeleceng.2011.11.005","article-title":"Automatic recognition vision system guided for apple harvesting robot","volume":"38","author":"Ji","year":"2012","journal-title":"Comput. Electr. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5684","DOI":"10.1016\/j.ijleo.2014.07.001","article-title":"Automatic method of fruit object extraction under complex agricultural background for vision system of fruit picking robot","volume":"125","author":"Wei","year":"2014","journal-title":"Optik"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yao, J., Qi, J., Zhang, J., Shao, H., Yang, J., and Li, X. (2021). A Real-Time Detection Algorithm for Kiwifruit Defects Based on YOLOv5. Electronics, 10.","DOI":"10.3390\/electronics10141711"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yan, B., Fan, P., Lei, X., Liu, Z., and Yang, F. (2021). A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5. Remote Sens., 13.","DOI":"10.3390\/rs13091619"},{"key":"ref_17","first-page":"116556","article-title":"Real-Time Visual Localization of the Picking Points for a Ridge-Planting Strawberry Harvesting Robot","volume":"10","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1109\/TPAMI.2019.2956516","article-title":"Cascade R-CNN: High Quality Object Detection and Instance Segmentation","volume":"43","author":"Cai","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yan, N., Zhou, T., Gu, C., Jiang, A., and Lu, W. (2020, January 10\u201313). Instance Segmentation Model for Substation Equipment Based on Mask R-CNN * 2020. Proceedings of the 2020 International Conference on Electrical Engineering and Control Technologies, Melbourne, VIC, Australia.","DOI":"10.1109\/CEECT50755.2020.9298600"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, H., Liang, H., Ni, T., Huang, L., and Yang, J. (2021). Research on Multi-Object Sorting System Based on Deep Learning. Sensors, 21.","DOI":"10.3390\/s21186238"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2014). Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE T Pattern Anal., 1904\u20131916.","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ismail, A., Elpeltagy, M., Zaki, M.S., and Eldahshan, K.A. (2021). A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost. Sensors, 21.","DOI":"10.3390\/s21165413"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Takahashi, Y., Gu, Y., Nakada, T., Abe, R., and Nakaguchi, T. (2021). Estimation of Respiratory Rate from Thermography Using Respiratory Likelihood Index. Sensors, 21.","DOI":"10.3390\/s21134406"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, H., Chen, C., Tsai, Y., Hsieh, K., and Lin, H. (2021). Identifying Images of Dead Chickens with a Chicken Removal System Integrated with a Deep Learning Algorithm. Sensors, 21.","DOI":"10.3390\/s21113579"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single shot multibox detector. Proceedings of the Computer Vision\u2014ECCV 2016, Lecture Notes in Computer Science, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Su, Y., and Yan, P. (2020, January 10\u201313). A defect detection method of gear end-face based on modified YOLO-V3. Proceedings of the 2020 10th Institute of Electrical and Electronics Engineers International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, Xi\u2019an, China.","DOI":"10.1109\/CYBER50695.2020.9279161"},{"key":"ref_28","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, G., Nouaze, J.C., Mbouembe, P.L., and Kim, J.H. (2021). YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3. Sensors, 20.","DOI":"10.3390\/s20072145"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.biosystemseng.2019.11.017","article-title":"Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector","volume":"189","author":"Wu","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhao, L., and Li, S. (2020). Object Detection Algorithm Based on Improved YOLOv3. Electronics, 9.","DOI":"10.3390\/electronics9030537"},{"key":"ref_32","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rahman, E.U., Zhang, Y., Ahmad, S., Ahmad, H.I., and Jobaer, S. (2021). Autonomous Vision-Based Primary Distribution Systems Porcelain Insulators Inspection Using UAVs. Sensors, 21.","DOI":"10.3390\/s21030974"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Spyridis, Y., Lagkas, T.D., Sarigiannidis, P.G., Argyriou, V., Sarigiannidis, A., Eleftherakis, G., and Zhang, J. (2021). Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks. Sensors, 21.","DOI":"10.3390\/s21113936"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Famiglietti, N.A., Cecere, G., Grasso, C., Memmolo, A., and Vicari, A. (2021). A Test on the Potential of a Low Cost Unmanned Aerial Vehicle RTK\/PPK Solution for Precision Positioning. Sensors, 21.","DOI":"10.3390\/s21113882"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Dufour, D., Noc, L.L., Tremblay, B., Tremblay, M., G\u00e9n\u00e9reux, F., Terroux, M., Vachon, C., Wheatley, M.J., Johnston, J.M., and Wotton, M. (2021). A Bi-Spectral Microbolometer Sensor for Wildfire Measurement. Sensors, 21.","DOI":"10.3390\/s21113690"},{"key":"ref_37","unstructured":"Ma, N., Zhang, X., and Sun, J. (2007). Funnel Activation for Visual Recognition. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/682\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:02:19Z","timestamp":1760133739000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/682"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,17]]},"references-count":37,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22020682"],"URL":"https:\/\/doi.org\/10.3390\/s22020682","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,1,17]]}}}