Abstract: The teat count in a sow can serve as a key reproductive phenotype, thus offering valuable insights for selective breeding in a vital component of PSY (pigs weaned per sow per year). There is a positive correlation between the number of teats in pigs and their litter size. Particularly, the teat count in piglets was closely aligned with the average of their parents. Consequently, it is very necessary to select the piglets using teat count. The symmetry and shape of a sow's teats are two of the most important indicators of nursing ability. The better lactation performance was represented by the more orderly and regular arrangements. However, it is still challenging to capture clear videos of an adult sow's abdomen, due to the potential interference from stains. Automatic teat counting is also required for the labor-saving, higher efficiency, and higher accuracy, compared with the manual. In this study, a deep learning-based approach was proposed for the teat counting and evaluation using videos of piglets' abdomens. Among them, there was consistency in the number of teats from birth to adulthood of female piglets. Specifically, a camera was first installed on the piglet management platform, in order to capture the videos of the abdomen of piglets (2-7 days old). Then, these videos were screened using clarity. A sequence of images was preprocessed to facilitate the automatic teat counting via an enhanced Pignip-YOLOv5s object detection network. A sliding window majority voting mechanism was applied to the teat count sequence to acquire the final tally for high counting accuracy. Experimental results show that the improved Pignip-YOLOv5s achieved a mean average precision (mAP) of 0.97. Better performance was also obtained in the challenging conditions, such as tightly spaced teats at the piglet's abdominal end, complex body textures, and obscure vision from the umbilical cord and the shadow. The higher robustness was observed, compared with the original. There was an accuracy rate of 90.26% for teat counting in the dataset of 113 piglet abdomen videos. Some parameters were selected to quantify the piglet teat morphology, such as the number of paired teats and the distance between teats. A teat classification was also established for the left and right teats, according to the teat positions obtained from the Pignip-YOLOv5s object detection network. The image was divided into the quartile regions. The teat midpoints in each region were calculated to classify the left and right teats on the piglet belly. Additionally, a teat pairing algorithm was introduced to identify the teats in pairs, in order to calculate the pairing rates for the other data of teat morphology. A practical value was offered for the piglet teat counting and morphology assessment using images of the piglet abdomen. In summary, the target detection-based piglet teat counting and mammary evaluation can serve as a novel and effective way to extract breeding indicators in the livestock breeding industry. The finding can also provide high accuracy, speed, and efficiency in the realm of boar breeding.