Abstract: Defects are required to be removed in many rows of dried Hami jujubes. In this study, an online detection and counting system was designed for dried Hami jujubes that were randomly distributed on the conveyor belt. The system was divided into four parts, including a target detector, multi-target tracker, counter, and software interface. The target detector was used to detect the defect categories of dried Hami jujube and then mark the detection box. The multi-target tracker was used to distribute the identity information of dried Hami jujube in the video. The counter was used to count the number of dried Hami jujube in each defect category. The software interface was used to control the program execution. The accuracy of multi-target tracking depended mainly on target detection. The target detector was first designed to compare several models of YOLO series, in order to better detect the defects with high counting accuracy. The better detection of defects was achieved in the YOLOv7 model. After that, the 3×3 CBS convolution layer in the YOLOv7 model was replaced with DCNv2, DWconv, PConv, GSconv, and DSconv. A comparison was made to explore the influence of the improved model on the defect detection of dried Hami jujube. It was found that the DSconv convolution of the improved YOLOv7 model shared fewer parameters, the higher detection speed, and the better accuracy, compared with the original. Four attention mechanisms were compared, including CBAM, ECA, SE, and SimAM. ELAN and SPP layers were also added to the backbone network of the YOLOv7 model. SimAM was performed the best to increase the accuracy of target detection. The ByteTrack multi-target tracker was selected to deal with the high similarity in the different kinds of dried Hami jujubes under the actual environment. The reason was that the appearance information dominated the performance of the multi-target detector for data association. A line-drawing counting was also proposed for statistical counting. The coordinates and ID information were focused at both ends of the counting line to count. Finally, the counting performance was achieved for dried Hami jujube. The software interface was designed using PyQt5 in the practical application. The model was then verified on the detection system of dried Hami jujube. The improved model was also deployed into the detection and counting system for online detection and counting of dried Hami jujube. The experimental results show that: 1) The floating-point computation of the improved YOLOv7 model was 64.6 G, which was 38.6% lower than that of the YOLOv7 model. The mAP, Recall, and F₁-score on the test set reached 98.03%, 93.43%, and 95.00%, respectively, which increased by 4.40, 6.88, and 7.00 percent points, respectively, compared with the original. 2) The counting model with ByteTrack as the target detector showed an accuracy rate of 90.12% for the multi-category counting. The finding can also provide technical support for the detection, counting, sorting, and grading of dried Hami jujube.