In the context of real-world observations of water purification flocculation processes, current image segmentation-based methods for detecting floc features face several challenges, which include poor recognition accuracy for deep-lying flocs, high annotation costs, and difficulties in adaptively processing depth-of-field information aiming at these problems, a new floc feature detection method based on improved density map and locally enhanced convolutional neural network (LECNN) is proposed. First, a density map construction method based on multipoint marking and average kernel smoothing is designed, to address the inability of the density map to simultaneously reflect multiple floc feature parameters. Second, a scene depth adaptive structure that assigns different weights to flocs at various depths is proposed, to mitigate the inaccuracies in floc parameter detection caused by parallax. Then, the proposed LECNN captures multiscale receptive fields while emphasizing local features. In comparative tests on a floc image dataset with multipoint markings, LECNN demonstrates accurate and robust density map fitting performance against recently proposed pixel-level prediction network structures, achieving a performance improvement over other floc feature detection benchmark methods in experimental results.