A passion fruit counting method based on the lightweight YOLOv5s and improved DeepSORT

Abstract

Accurate yield estimation of passion fruits is essential for planning acreage and harvest timing. However, due to the complexity of the natural environment and tracking instability, the existing yield estimation methods suffer from excessively large models that are difficult to deploy or repetitive counting of fruit. Therefore, an improved approach for efficient passion fruit yield estimation was proposed using the lightweight YOLOv5s and improved DeepSORT. First, the video is fed into the proposed lightweight YOLOv5s called YOLOv5s-little to obtain coordinates and confidence information about the fruits within each frame. Then, the information obtained from the detection model is input into improved DeepSORT for continuous frame tracking of passion fruit. Considering the frequent error IDs (ID switching), two improvements based on DeepSORT are proposed: delaying the creation of tracks and adding a second round of IoU matching. Finally, to overcome the problem of repetitive counting, a specific tracking counting method based on the track information and state is used for accurate passion fruit counting. Our method achieved a competitive result in tests. YOLOv5s-little detector achieved precision of 98.9%, 98.3% recall, 99.5% mAP, and only 0.9MB model size. The improved DeepSORT algorithm achieved higher order tracking accuracy (HOTA) of 79.6%, multi-object tracking accuracy (MOTA) of 92.58%, identification F1 (IDF1) of 95.02%, and ID switch (IDSW) of 11 respectively. Compared with DeepSORT, it improved by 4.66%, 1.8%, and 9.16% in HOTA, MOTA and IDF1, respectively, and IDSW improved the most with 85%. Compared with FairMOT and TransTrack, the HOTA of YOLOv5s-little + improved DeepSORT achieved improvements of 11.56% and 25.24%, respectively. The statistical average counting accuracy of our proposed counting method reaches 95.1%, which is a 7.09% improvement over the maximum ID value counting method. The counting results from test videos are highly correlated with the manual counting results (

({text{R}}^{2})

 = 0.96), indicating that the counting method has high accuracy and effectiveness. These results show that YOLOv5s-little + improved DeepSORT can meet the practical needs of passion fruit yield estimation in real scenarios.

Abstract
Accurate yield estimation of passion fruits is essential for planning acreage and harvest timing. However, due to the complexity of the natural environment and tracking instability, the existing yield estimation methods suffer from excessively large models that are difficult to deploy or repetitive counting of fruit. Therefore, an improved approach for efficient passion fruit yield estimation was proposed using the lightweight YOLOv5s and improved DeepSORT. First, the video is fed into the proposed lightweight YOLOv5s called YOLOv5s-little to obtain coordinates and confidence information about the fruits within each frame. Then, the information obtained from the detection model is input into improved DeepSORT for continuous frame tracking of passion fruit. Considering the frequent error IDs (ID switching), two improvements based on DeepSORT are proposed: delaying the creation of tracks and adding a second round of IoU matching. Finally, to overcome the problem of repetitive counting, a specific tracking counting method based on the track information and state is used for accurate passion fruit counting. Our method achieved a competitive result in tests. YOLOv5s-little detector achieved precision of 98.9%, 98.3% recall, 99.5% mAP, and only 0.9MB model size. The improved DeepSORT algorithm achieved higher order tracking accuracy (HOTA) of 79.6%, multi-object tracking accuracy (MOTA) of 92.58%, identification F1 (IDF1) of 95.02%, and ID switch (IDSW) of 11 respectively. Compared with DeepSORT, it improved by 4.66%, 1.8%, and 9.16% in HOTA, MOTA and IDF1, respectively, and IDSW improved the most with 85%. Compared with FairMOT and TransTrack, the HOTA of YOLOv5s-little + improved DeepSORT achieved improvements of 11.56% and 25.24%, respectively. The statistical average counting accuracy of our proposed counting method reaches 95.1%, which is a 7.09% improvement over the maximum ID value counting method. The counting results from test videos are highly correlated with the manual counting results (

({text{R}}^{2})

 = 0.96), indicating that the counting method has high accuracy and effectiveness. These results show that YOLOv5s-little + improved DeepSORT can meet the practical needs of passion fruit yield estimation in real scenarios. Leer más