Analyzing the cone photoreceptor pattern in images obtained from the living human retina using quantitative methods can be crucial for the early detection and management of various eye conditions. Confocal adaptive optics scanning light ophthalmoscope (AOSLO) imaging enables visualization of the cones from reflections of waveguiding cone photoreceptors. While there have been significant improvements in automated algorithms for segmenting cones in confocal AOSLO images, the process of labeling data remains labor-intensive and manual. This paper introduces a method based on deep learning (DL) for detecting and segmenting cones in AOSLO images. The models were trained on a semi-automatically labeled dataset of 20 AOSLO batches of images of 18 participants for 0 \(^{\circ }\) , 1 \(^{\circ }\) , and 2 \(^{\circ }\) from the foveal center. F1 scores were 0.968, 0.958, and 0.954 for 0 \(^{\circ }\) , 1 \(^{\circ }\) , and 2 \(^{\circ }\) , respectively, which is better than previously reported DL approaches. Our method minimizes the need for labeled data by only necessitating a fraction of labeled cones, which is especially beneficial in the field of ophthalmology, where labeled data can often be limited.